Sewage treatment plant management method, device and equipment based on intelligent diagnosis

Abstract

The invention belongs to the technical field of intelligent environmental protection, and discloses a sewage treatment plant management method, device and equipment based on intelligent diagnosis. The method comprises the following steps: constructing an evaluation model of sewage treatment plant operation management; using the evaluation model to score the management of the sewage treatment plant; if the score is lower than a preset score, obtaining operation management data associated with water quality from the sewage treatment plant; diagnosing the sewage treatment plant according to the operation management data; and regulating and controlling the sewage treatment plant according to the diagnosis result. In this way, the sewage treatment plant is managed in combination with the evaluation method, and the management level and management efficiency of the sewage treatment plant are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent environmental protection technology, and in particular to a sewage treatment plant management method, device and equipment based on intelligent diagnosis. Background Art

[0002] The operation and management of sewage treatment plants is a crucial part of the implementation of production and operation management, and is characterized by systematicity, legality and scientificity. The operation and management of sewage treatment plants is not only an important measure for the internal control of quality, safety and environment of sewage treatment plants, but also a presentation of the requirements of external policies and natural environment for their production results.

[0003] The current management method for sewage treatment plants is to manually supervise the equipment, environment, water quality, etc. of the sewage treatment plants. However, the manual method requires sufficient professional and technical personnel. The current management method lacks a high management level, and due to the low degree of automation, it also affects the management efficiency.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present invention is to provide a sewage treatment plant management method, device and equipment based on intelligent diagnosis, aiming to solve the technical problems that the current management method lacks a high management level and the low degree of automation also affects the management efficiency.

[0006] To achieve the above object, the present invention provides a sewage treatment plant management method based on smart diagnosis, and the sewage treatment plant management method based on smart diagnosis includes the following steps:

[0007] Construct an evaluation model for the operation and management of sewage treatment plants;

[0008] Using the evaluation model to score the management of the sewage treatment plant;

[0009] If the score is lower than a preset score, obtaining operation management data related to water quality from the sewage treatment plant;

[0010] diagnosing the sewage treatment plant according to the operation management data;

[0011] The sewage treatment plant is regulated according to the diagnosis results.

[0012] In some embodiments, the construction of an evaluation model for operation and management of a sewage treatment plant includes:

[0013] Collect the historical evaluation information of each expert and construct corresponding judgment matrices respectively to obtain multiple judgment matrices;

[0014] Calculating the geometric mean of multiple judgment matrices through a preset function, and constructing a target judgment matrix according to the geometric mean;

[0015] Normalizing and summing the target judgment matrix to obtain the weight value of each evaluation index;

[0016] Determine a target judgment matrix eigenvector based on the weight value;

[0017] Performing consistency check by using the target judgment matrix and the matrix eigenvector;

[0018] After the consistency check is passed, the evaluation model is determined based on the weight values ​​of the various evaluation indicators.

[0019] In some embodiments, the performing consistency check on the target judgment matrix and the matrix eigenvector includes:

[0020] Calculate the target characteristic root according to the target judgment matrix and the target judgment matrix characteristic vector;

[0021] Calculating a consistency index according to the target characteristic root;

[0022] determining a random consistency ratio according to the consistency indicator;

[0023] If the random consistency ratio is less than the preset ratio, the consistency check is determined to be qualified.

[0024] In some embodiments, the using the evaluation model to score the management of the sewage treatment plant includes:

[0025] Get the weight value corresponding to each evaluation indicator;

[0026] Using the evaluation model and based on the weight values, respectively obtain a quality management score, a health management score, a safety management score, and an environmental management score, wherein quality management, health management, safety management, and environmental management correspond to a number of evaluation indicators, respectively;

[0027] A target score for the sewage treatment plant is determined based on the quality management score, the health management score, the safety management score, and the environmental management score.

[0028] In some embodiments, the diagnosing the sewage treatment plant according to the operation management data includes:

[0029] Acquire current effluent data and historical dosing data according to the operation management data;

[0030] Determine the target control parameter according to the current effluent data and the historical dosing data;

[0031] Performing parameter comparison between the target control parameter and the current control parameter;

[0032] The sewage treatment plant is diagnosed based on the comparison results.

[0033] In some embodiments, regulating the sewage treatment plant according to the diagnosis result includes:

[0034] determining a parameter difference between a target control parameter and a current control parameter based on the diagnosis result;

[0035] Determine a parameter type difference and a parameter size difference between the target control parameter and the current control parameter based on the parameter difference;

[0036] Determine a control strategy according to the parameter type difference and the parameter size difference;

[0037] The sewage treatment plant is regulated according to the regulation strategy.

[0038] In some embodiments, determining the control strategy according to the parameter type difference and the parameter size difference includes:

[0039] Determining a parameter control method according to the parameter type difference;

[0040] Determine the parameter control amplitude and parameter control direction according to the parameter size difference;

[0041] A control strategy is determined based on the parameter control method, the parameter control amplitude, and the parameter control direction.

[0042] In addition, to achieve the above-mentioned purpose, the present invention also proposes a sewage treatment plant management device based on intelligent diagnosis, and the sewage treatment plant management device based on intelligent diagnosis includes:

[0043] Construction module, used to build an evaluation model for the operation and management of sewage treatment plants;

[0044] An evaluation module, used to score the management of the sewage treatment plant using the evaluation model;

[0045] A collection module, for obtaining operation management data associated with water quality from the sewage treatment plant if the score is lower than a preset score;

[0046] A diagnosis module, used for diagnosing the sewage treatment plant according to the operation management data;

[0047] A control module is used to regulate the sewage treatment plant according to the diagnosis result.

[0048] In some embodiments, the construction module is used to collect historical evaluation information of each expert and construct corresponding judgment matrices respectively to obtain multiple judgment matrices;

[0049] Calculating the geometric mean of multiple judgment matrices through a preset function, and constructing a target judgment matrix according to the geometric mean;

[0050] Normalizing and summing the target judgment matrix to obtain the weight value of each evaluation index;

[0051] Determine a target judgment matrix eigenvector based on the weight value;

[0052] Performing consistency check by using the target judgment matrix and the target judgment matrix eigenvector;

[0053] After the consistency check is passed, the evaluation model is determined based on the weight values ​​of the various evaluation indicators.

[0054] In addition, to achieve the above-mentioned objectives, the present invention also proposes a sewage treatment plant management device based on intelligent diagnosis, and the sewage treatment plant management device based on intelligent diagnosis includes: a memory, a processor, and a sewage treatment plant management program based on intelligent diagnosis stored in the memory and executable on the processor, and the sewage treatment plant management program based on intelligent diagnosis is configured to implement the steps of the sewage treatment plant management method based on intelligent diagnosis as described above.

[0055] The present invention constructs an evaluation model for the operation and management of a sewage treatment plant; uses the evaluation model to score the management of the sewage treatment plant; if the score is lower than a preset score, obtains operation and management data associated with water quality from the sewage treatment plant; diagnoses the sewage treatment plant according to the operation and management data; and regulates the sewage treatment plant according to the diagnosis result. In the above manner, the sewage treatment plant is managed in combination with the evaluation method, thereby improving the management level and efficiency of the sewage treatment plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the first embodiment of the sewage treatment plant management method based on intelligent diagnosis of the present invention;

[0057] Figure 2 It is a schematic diagram of the sewage treatment plant operation management evaluation system in the sewage treatment plant management method based on intelligent diagnosis of the present invention;

[0058] Figure 3 This is a structural block diagram of the first embodiment of the sewage treatment plant management device based on intelligent diagnosis of the present invention.

[0059] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0061] The embodiment of the present invention provides a sewage treatment plant management method based on intelligent diagnosis, referring to Figure 1 , Figure 1 This is a flow chart of a first embodiment of a sewage treatment plant management method based on intelligent diagnosis according to the present invention.

[0062] In this embodiment, the sewage treatment plant management method based on intelligent diagnosis includes the following steps:

[0063] Step S10: construct an evaluation model for the operation and management of the sewage treatment plant.

[0064] In this embodiment, the executor of this embodiment is a sewage treatment plant management device based on intelligent diagnosis, wherein the sewage treatment plant management device based on intelligent diagnosis has functions such as data processing, data communication and program running. The industrial park wastewater treatment diagnosis equipment based on the intelligent environmental protection platform can be a computer terminal device or other network device, and of course it can also be other devices with similar functions, and this embodiment does not limit this.

[0065] It should be noted that the operation and management of sewage treatment plants is a crucial part of the implementation of production and operation management, and has the characteristics of systematic, legal and scientific nature. The operation and management of sewage treatment plants is not only an important measure for the internal control of quality, safety and environment of sewage treatment plants, but also a presentation of the requirements of external policies and the natural environment for their production effectiveness. The current management method for sewage treatment plants is to manually supervise the equipment, environment and water quality of sewage treatment plants. However, the manual method requires sufficient professional and technical personnel. The current management method lacks a high level of management, and due to the low degree of automation, it also affects the management efficiency.

[0066] In order to solve the above problems, in this embodiment, an evaluation model for the operation and management of a sewage treatment plant is constructed; the management of the sewage treatment plant is scored using the evaluation model; if the score is lower than the preset score, the operation and management data associated with the water quality is obtained from the sewage treatment plant; the sewage treatment plant is diagnosed according to the operation and management data; and the sewage treatment plant is regulated according to the diagnosis results. Through the above method, the sewage treatment plant is managed in combination with the evaluation method, which improves the management level and efficiency of the sewage treatment plant, which can be specifically achieved in the following ways.

[0067] In the specific implementation, in this embodiment, an evaluation method is added to the management of the sewage treatment plant to improve the management level and management efficiency. Before the evaluation, it is necessary to build an evaluation model for the operation and management of the sewage treatment plant.

[0068] The construction process of the evaluation model is to collect the historical evaluation information of each expert, construct the corresponding judgment matrix respectively, to obtain multiple judgment matrices; calculate the geometric mean of multiple judgment matrices through a preset function, and construct a target judgment matrix according to the geometric mean; normalize and sum the target judgment matrix to obtain the weight value of each evaluation index; determine the target judgment matrix eigenvector based on the weight value; perform consistency check between the target judgment matrix and the matrix eigenvector; after the consistency check is qualified, determine the evaluation model based on the weight value of each evaluation index.

[0069] Specifically, in this embodiment, historical evaluation information of multiple experts is collected to construct a judgment matrix. For example, assuming that historical evaluation information of 10 experts is collected, judgment matrices A1, A2, ..., A3 can be constructed for each expert's historical evaluation information. 10 , where a judgment matrix is ​​shown below:

[0070]

[0071] Then the average value is calculated by the preset function, and the final target matrix is ​​constructed based on the calculated average value. Assume that the target matrix is ​​A, and the element values ​​in A are A1, A2, ..., A 10 The average value calculated after substituting into the preset function, it should be noted that the preset function used is the geomean function, of course, other functions can also be used to calculate the average value, which is not limited in this embodiment.

[0072] For the obtained target judgment matrix, further normalization and summation are performed to calculate the weight value of each evaluation index. For example, assuming that the target judgment matrix is ​​A, the target judgment matrix A is as follows:

[0073]

[0074] Through the above A, we can get the weight value table corresponding to each evaluation index, as shown below:

[0075] A <![CDATA[B1]]> <![CDATA[B2]]> <![CDATA[B3]]> W <![CDATA[B1]]> 1 3.2 1.2 0.43 <![CDATA[B2]]> 0.28 1 0.9 0.11 <![CDATA[B3]]> 0.34 2.5 3.5 0.33

[0076] In the above table, B1 to B3 represent different evaluation dimensions, and W is the weight value, which is used to construct the target judgment matrix eigenvector, for example, W = (0.43, 0.11, 0.33) TFinally, a consistency check is performed. If the consistency check result is qualified, an evaluation model can be obtained according to the weight values ​​of the above evaluation indicators, and the above weight values ​​can be substituted into the sewage treatment plant operation management evaluation system. In this embodiment, the sewage treatment plant operation management evaluation system is as follows: Figure 2 As shown, the value corresponding to W can be substituted into the sum of multiple evaluation indicators of B1, B2 and B3, which is the weight value corresponding to W. For example, multiple evaluation indicators of B1 include B 11 , B12 and B13, B 11 +B12+B13=0.43, B2 and B3 are similar, B 21 +B 22 +B 23 =0.11, B 31 +B 32 +B 33 =033, you can refer to Figure 2 shown.

[0077] In some embodiments, the consistency check process is to calculate the target characteristic root based on the target judgment matrix and the target judgment matrix eigenvector; calculate the consistency index based on the target characteristic root; determine the random consistency ratio based on the consistency index; if the random consistency ratio is less than the preset ratio, the consistency check is determined to be qualified.

[0078] The target characteristic root is the largest characteristic root. The calculation formula of the target characteristic root is as follows: max =A / W, where A is the target judgment matrix, W is the target judgment matrix eigenvector, and the consistency index calculation formula is as follows: n is the number of indicators, and the formula for calculating the random consistency ratio is as follows: The RI value can be 0.9, or can be set to other values ​​according to actual needs, and the preset ratio can be set to 0.1.

[0079] Step S20: using the evaluation model to score the management of the sewage treatment plant.

[0080] After completing the construction of the above-mentioned evaluation model, the model can be used in this embodiment to score the management of the sewage treatment plant. In this embodiment, the evaluation dimensions are divided into four dimensions, including quality management, health management, safety management and environmental management. Each dimension has several evaluation indicators. For example, quality management includes production operation evaluation indicators, process technology evaluation indicators, water quality analysis management evaluation indicators and equipment comprehensive management evaluation indicators. These evaluation indicators can be pre-set according to actual needs. This embodiment is only for illustration and is not used to limit this embodiment.

[0081] The evaluation model can be used to obtain the score corresponding to each dimension, such as the quality management score, health management score, safety management score and environmental management score. In this embodiment, a corresponding weight can be set for each dimension, and the target score is obtained by weighted calculation. The weighted calculation method is not described in detail in this embodiment. In this embodiment, the accuracy and rationality of the score can be guaranteed by the division of the above dimensions.

[0082] Step S30: If the score is lower than a preset score, obtaining operation management data associated with water quality from the sewage treatment plant.

[0083] The preset score can be set to 80 points or 90 points, and can also be adjusted according to management needs. If it is lower than the preset score, it means that the management of the sewage treatment plant does not meet the management requirements. It is easy to understand that when the management of the sewage treatment plant does not meet the management requirements, it directly affects the water quality of the effluent. Therefore, in this case, it is necessary to obtain operation management data related to water quality in this embodiment.

[0084] Step S40: diagnose the sewage treatment plant according to the operation management data.

[0085] In a specific implementation, the diagnostic process is to obtain current effluent data and historical drug addition data based on the operation management data; determine target control parameters based on the current effluent data and historical drug addition data; perform parameter comparison between the target control parameters and the current control parameters; and diagnose the sewage treatment plant based on the comparison results.

[0086] In this embodiment, the current effluent data and historical dosing data can be input into a pre-constructed water quality control system model to obtain target control parameters. The construction process of the water quality control system model in this embodiment is specifically to construct a sample data set based on uncontrollable factors and controllable factors, and the controllable factors include source water data and dosing data; process the sample data set, and divide the training set and the test set based on the processed sample data set; construct a kernel function, and use the kernel function to perform high-dimensional space mapping on the training set and then input it as input data into the preset model for training; determine the model parameter set, and use the network search method to optimize the parameters during the model training process based on the model parameter set to obtain reference model parameters; use the test set to evaluate the reference model parameters, use the qualified reference model parameters as the target model parameters, and construct a water quality control system model based on the target model parameters.

[0087] Specifically, the uncontrollable factors in this embodiment include factors such as weather, and the controllable factors include source water data and dosing data, such as flow, turbidity, temperature, and pH value of source water, and dosing data such as dosing amount and dosing frequency. The division of training set and test set is, for example, 10,000 groups of sample data sets, which are subdivided into training and prediction sets, of which 5,000 groups are used as training set samples, and the remaining 5,000 groups of sample data are used as test sets. The model parameter set can select inlet water quality parameters, outlet water quality parameters, and environmental factor parameters, etc., and then adopt a network search method to optimize parameters during the model training process to obtain reference model parameters, and finally input the reference model parameters into the trained model, and compare the output results with the test set to complete the evaluation.

[0088] In some embodiments, the kernel function is composed of a first function, a second function, and a preset error, wherein the first function is a support vector machine related function, such as or, The second function is a loss function, for example, ef((x)-y)=max(0,|f(xy)|-β) or ef((x)-y)=f((x)-y) 2 , and then construct a kernel function based on the preset error, the first function and the second function, for example, h = e k (α+y k -aK(x i ,x k )), where e k represents the second function, K(x i ,x k ) represents the first function, α represents the preset error, and it should be emphasized that the first function and the second function are arbitrarily selected from the above functions.

[0089] Step S50: regulating the sewage treatment plant according to the diagnosis result.

[0090] In specific implementation, scoring and diagnosis can be used to accurately identify problems in the management of sewage treatment plants, which can serve as a basis for subsequent regulation.

[0091] Specifically, according to the diagnosis result, the parameter difference between the target control parameter and the current control parameter can be determined, and the parameter difference includes parameter type difference and parameter size difference. Then the control strategy is determined, and finally the sewage treatment plant is controlled according to the determined control strategy.

[0092] In some embodiments, the process of determining the control strategy is to determine the parameter control method according to the parameter type difference; determine the parameter control amplitude and parameter control direction according to the parameter size difference; determine the control strategy based on the parameter control method, the parameter control amplitude and the parameter control direction. Specifically, the parameter control method physical parameter control method, chemical parameter control method and biological parameter control method can be determined by the parameter type difference. The parameter size difference directly determines the parameter control amplitude. According to the parameter size difference, it can be determined whether the parameter difference is a positive value or a negative value. If it is a positive value, it means that the current control parameter is greater than the current control parameter, and the parameter control direction is to increase the current control parameter.

[0093] This embodiment constructs an evaluation model for the operation and management of a sewage treatment plant; uses the evaluation model to score the management of the sewage treatment plant; if the score is lower than the preset score, obtains operation and management data related to water quality from the sewage treatment plant; diagnoses the sewage treatment plant based on the operation and management data; and regulates the sewage treatment plant based on the diagnosis results. In the above manner, the sewage treatment plant is managed in combination with the evaluation method, thereby improving the management level and efficiency of the sewage treatment plant.

[0094] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the sewage treatment plant management device based on intelligent diagnosis of the present invention.

[0095] like Figure 3 As shown, the sewage treatment plant management device based on intelligent diagnosis proposed in the embodiment of the present invention includes:

[0096] The construction module 10 is used to construct an evaluation model for the operation and management of the sewage treatment plant.

[0097] The evaluation module 20 is used to score the management of the sewage treatment plant using the evaluation model.

[0098] The collection module 30 is used to obtain operation management data associated with water quality from the sewage treatment plant if the score is lower than a preset score.

[0099] The diagnosis module 40 is used to diagnose the sewage treatment plant according to the operation management data.

[0100] The control module 50 is used to regulate the sewage treatment plant according to the diagnosis result.

[0101] This embodiment constructs an evaluation model for the operation and management of a sewage treatment plant; uses the evaluation model to score the management of the sewage treatment plant; if the score is lower than the preset score, obtains operation and management data related to water quality from the sewage treatment plant; diagnoses the sewage treatment plant based on the operation and management data; and regulates the sewage treatment plant based on the diagnosis results. In the above manner, the sewage treatment plant is managed in combination with the evaluation method, thereby improving the management level and efficiency of the sewage treatment plant.

[0102] In some embodiments, the construction module 10 is used to collect historical evaluation information of each expert, and construct corresponding judgment matrices respectively to obtain multiple judgment matrices; calculate the geometric mean of multiple judgment matrices through a preset function, and construct a target judgment matrix based on the geometric mean; normalize and sum the target judgment matrix to obtain the weight value of each evaluation indicator; determine the target judgment matrix eigenvector based on the weight value; perform consistency check between the target judgment matrix and the target judgment matrix eigenvector; after the consistency check is qualified, determine the evaluation model based on the weight value of each evaluation indicator.

[0103] In some embodiments, the construction module 10 is used to calculate the target characteristic root based on the target judgment matrix and the matrix eigenvector; calculate the consistency index based on the target characteristic root; determine the random consistency ratio based on the consistency index; if the random consistency ratio is less than the preset ratio, the consistency check is determined to be qualified.

[0104] In some embodiments, the evaluation module 20 is used to obtain the weight values ​​corresponding to each evaluation indicator; use the evaluation model to obtain the quality management score, health management score, safety management score and environmental management score based on the weight values, wherein quality management, health management, safety management and environmental management correspond to several evaluation indicators respectively; determine the target score of the sewage treatment plant based on the quality management score, the health management score, the safety management score and the environmental management score.

[0105] In some embodiments, the diagnostic module 40 is used to obtain current effluent data and historical drug addition data based on the operation management data; determine target control parameters based on the current effluent data and historical drug addition data; perform parameter comparison between the target control parameters and the current control parameters; and diagnose the sewage treatment plant based on the comparison results.

[0106] In some embodiments, the control module 50 is used to determine the parameter difference between the target control parameter and the current control parameter based on the diagnosis result; determine the parameter type difference and the parameter size difference between the target control parameter and the current control parameter based on the parameter difference; determine the control strategy according to the parameter type difference and the parameter size difference; and control the sewage treatment plant according to the control strategy.

[0107] In some embodiments, the control module 50 is used to determine the parameter control method according to the parameter type difference; determine the parameter control amplitude and parameter control direction according to the parameter size difference; and determine the control strategy based on the parameter control method, the parameter control amplitude and the parameter control direction.

[0108] An embodiment of the present application also provides a sewage treatment plant management device based on intelligent diagnosis, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and the memory is used to store computer programs; the processor is used to implement the above-mentioned sewage treatment plant management method based on intelligent diagnosis when executing the program stored in the memory.

[0109] The communication bus mentioned in the above-mentioned sewage treatment plant management equipment based on intelligent diagnosis can be a peripheral component interconnect standard (English: Peripheral Component Interconnect, referred to as: PCI) bus or an extended industry standard architecture (English: Extended Industry Standard Architecture, referred to as: EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0110] The communication interface is used for communication between the above-mentioned sewage treatment plant management equipment based on intelligent diagnosis and other equipment.

[0111] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0112] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.

[0113] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0114] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0115] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0116] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0117] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0118] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0119] In addition, for technical details that are not described in detail in this embodiment, please refer to the sewage treatment plant management method based on intelligent diagnosis provided in any embodiment of the present invention, which will not be repeated here.

[0120] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0121] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0122] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0123] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0124] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

Claims

1. A sewage treatment plant management method based on intelligent diagnosis, characterized in that: The sewage treatment plant management method based on intelligent diagnosis includes: Construct an evaluation model for the operation and management of sewage treatment plants; Using the evaluation model to score the management of the sewage treatment plant; If the score is lower than a preset score, obtaining operation management data related to water quality from the sewage treatment plant; diagnosing the sewage treatment plant according to the operation management data; The sewage treatment plant is regulated according to the diagnosis results.

2. The sewage treatment plant management method based on intelligent diagnosis according to claim 1, characterized in that: The evaluation model for the operation and management of the sewage treatment plant is constructed, including: Collect the historical evaluation information of each expert and construct corresponding judgment matrices respectively to obtain multiple judgment matrices; Calculating the geometric mean of multiple judgment matrices through a preset function, and constructing a target judgment matrix according to the geometric mean; Normalizing and summing the target judgment matrix to obtain the weight value of each evaluation index; Determine a target judgment matrix eigenvector based on the weight value; Performing consistency check by using the target judgment matrix and the target judgment matrix eigenvector; After the consistency check is passed, the evaluation model is determined based on the weight values ​​of the various evaluation indicators.

3. The sewage treatment plant management method based on intelligent diagnosis as claimed in claim 2, characterized in that: The performing consistency check on the target judgment matrix and the matrix eigenvector includes: Calculate the target characteristic root according to the target judgment matrix and the matrix characteristic vector; Calculating a consistency index according to the target characteristic root; determining a random consistency ratio according to the consistency indicator; If the random consistency ratio is less than the preset ratio, the consistency check is determined to be qualified.

4. The sewage treatment plant management method based on intelligent diagnosis according to claim 1, characterized in that: The using of the evaluation model to score the management of the sewage treatment plant includes: Get the weight value corresponding to each evaluation indicator; Using the evaluation model and based on the weight values, respectively obtain a quality management score, a health management score, a safety management score, and an environmental management score, wherein quality management, health management, safety management, and environmental management correspond to a number of evaluation indicators, respectively; A target score for the sewage treatment plant is determined based on the quality management score, the health management score, the safety management score, and the environmental management score.

5. The sewage treatment plant management method based on intelligent diagnosis according to claim 1, characterized in that: The diagnosing the sewage treatment plant according to the operation management data includes: Acquire current effluent data and historical dosing data according to the operation management data; Determine the target control parameter according to the current effluent data and the historical dosing data; Performing parameter comparison between the target control parameter and the current control parameter; The sewage treatment plant is diagnosed based on the comparison results.

6. The sewage treatment plant management method based on intelligent diagnosis according to claim 5, characterized in that: The regulating and controlling of the sewage treatment plant according to the diagnosis result includes: determining a parameter difference between a target control parameter and a current control parameter based on the diagnosis result; Determine a parameter type difference and a parameter size difference between the target control parameter and the current control parameter based on the parameter difference; Determine a control strategy according to the parameter type difference and the parameter size difference; The sewage treatment plant is regulated according to the regulation strategy.

7. The sewage treatment plant management method based on intelligent diagnosis according to claim 6, characterized in that: The determining of the control strategy according to the parameter type difference and the parameter size difference includes: Determining a parameter control method according to the parameter type difference; Determine the parameter control amplitude and parameter control direction according to the parameter size difference; A control strategy is determined based on the parameter control method, the parameter control amplitude, and the parameter control direction.

8. A sewage treatment plant management device based on intelligent diagnosis, characterized in that: The sewage treatment plant management device based on intelligent diagnosis includes: Construction module, used to build an evaluation model for the operation and management of sewage treatment plants; An evaluation module, used to score the management of the sewage treatment plant using the evaluation model; A collection module, for obtaining operation management data associated with water quality from the sewage treatment plant if the score is lower than a preset score; A diagnosis module, used for diagnosing the sewage treatment plant according to the operation management data; A control module is used to regulate the sewage treatment plant according to the diagnosis result.

9. The sewage treatment plant management device based on intelligent diagnosis according to claim 8, characterized in that: The construction module is used to collect historical evaluation information of each expert and construct corresponding judgment matrices respectively to obtain multiple judgment matrices; Calculating the geometric mean of multiple judgment matrices through a preset function, and constructing a target judgment matrix according to the geometric mean; Normalizing and summing the target judgment matrix to obtain the weight value of each evaluation index; Determine a target judgment matrix eigenvector based on the weight value; Performing consistency check by using the target judgment matrix and the target judgment matrix eigenvector; After the consistency check is passed, the evaluation model is determined based on the weight values ​​of the various evaluation indicators.

10. A sewage treatment plant management device based on intelligent diagnosis, characterized in that: The sewage treatment plant management device based on intelligent diagnosis includes: a memory, a processor, and a sewage treatment plant management program based on intelligent diagnosis stored in the memory and executable on the processor, and the sewage treatment plant management program based on intelligent diagnosis is configured to implement the steps of the sewage treatment plant management method based on intelligent diagnosis as described in any one of claims 1 to 7.

Images