A sewage treatment optimization operation method and system

Abstract

The present invention discloses a method and system for optimizing the operation of sewage treatment, and relates to the technical field of sewage treatment. The method includes: setting daily inspection nodes for sewage treatment equipment based on historical fault records; presetting a preliminary inspection cycle, and remotely acquiring inspection data of daily inspection nodes according to the inspection cycle; calculating the current stability coefficient of the equipment according to the real-time operation data of the sewage treatment equipment, and adjusting the inspection cycle based on the stability coefficient of the equipment; calculating the health index of the daily inspection node according to the obtained inspection data and the water quality data of the inlet and outlet of the sewage treatment equipment; generating an early warning report for the daily inspection nodes whose health index does not meet the standard; and the operation and maintenance personnel rush to the site to maintain and repair the daily inspection nodes mentioned in the early warning report. The sewage treatment equipment is accurately monitored to save labor costs; the inspection cycle is dynamically adjusted, the health mode of the equipment is automatically identified, and an early warning report is generated to solve the problem of untimely and inadequate equipment maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a sewage treatment optimization operation method and system. Background Art

[0002] With the development of sewage treatment technology, the importance of equipment maintenance has gradually been recognized. Sewage treatment plants and related enterprises have begun to pay attention to the daily operation and maintenance of equipment to ensure the stable operation of the equipment and extend its service life. The operation and maintenance technology of sewage treatment equipment has been continuously improved, including regular cleaning, inspection, replacement of parts and other routine maintenance measures. At the same time, some advanced operation and maintenance technologies and methods have also been introduced into the maintenance of sewage treatment equipment, such as online monitoring and fault diagnosis. These equipment operation and maintenance technologies play a vital role in the operation optimization of sewage treatment. However, the complexity of sewage treatment work and the diversity of equipment will still lead to a shortage of operation and maintenance personnel and untimely and inadequate equipment maintenance. Therefore, researchers in this field need to develop an equipment operation and maintenance technology with a higher operation and maintenance level and a more complete operation and maintenance process to optimize the operation process of sewage treatment. Summary of the invention

[0003] The present invention provides a sewage treatment optimization operation method, comprising:

[0004] Step 1. Set daily inspection nodes for sewage treatment equipment based on historical fault records;

[0005] Step 2: Preset a preliminary inspection cycle and remotely obtain the inspection data of daily inspection nodes according to the inspection cycle;

[0006] Step 3. Calculate the current stability coefficient of the equipment according to the real-time operation data of the sewage treatment equipment, and adjust the inspection cycle based on the stability coefficient of the equipment;

[0007] Step 4: Calculate the health index of daily inspection nodes based on the inspection data obtained and the water quality data at the inlet and outlet of the sewage treatment equipment;

[0008] Step 5: Generate early warning reports for daily inspection nodes whose health index does not meet the standard;

[0009] Step 6: Notify the operation and maintenance personnel to rush to the site to perform maintenance and repair on the daily inspection nodes mentioned in the early warning report.

[0010] The above-mentioned method for optimizing the operation of sewage treatment, wherein the current stability coefficient of the equipment is calculated according to the real-time operation data of the sewage treatment equipment, and the inspection cycle is adjusted based on the stability coefficient of the equipment, specifically includes the following sub-steps:

[0011] Regularly obtain real-time operation data of sewage treatment equipment;

[0012] Input the real-time operation data of the sewage treatment equipment into the stability coefficient calculation function to obtain the current stability coefficient of the equipment;

[0013] Adjust the inspection cycle according to the current stability coefficient of the sewage treatment equipment.

[0014] A sewage treatment optimization operation method as described above, wherein the health index of the daily inspection node is calculated based on the acquired inspection data and the water quality data at the inlet and outlet of the sewage treatment equipment, specifically comprising the following sub-steps:

[0015] Collect water quality data at the inlet and outlet of sewage treatment equipment;

[0016] Create a health index calculation function for daily inspection nodes;

[0017] The inspection data and water quality data are passed into the health index calculation function, and the current health index of the daily inspection node is returned.

[0018] A sewage treatment optimization operation method as described above, wherein a health index calculation function of a daily inspection node is created, specifically comprising the following sub-steps:

[0019] Create a water quality improvement index calculation formula based on the water quality parameters at the inlet and outlet of the sewage treatment equipment;

[0020] Create inspection node scoring formula based on the inspection data of sewage treatment equipment;

[0021] The water quality improvement index calculation formula and the inspection data scoring formula are integrated to form a health index calculation function.

[0022] The above-mentioned method for optimizing the operation of sewage treatment, in which a warning report is generated for daily inspection nodes whose health index does not meet the standard, specifically includes the following sub-steps:

[0023] Set a threshold for the health index based on the historical inspection data of daily inspection nodes;

[0024] Create a template for an early warning report;

[0025] Extract daily inspection nodes whose health index is lower than the threshold, and fill in the content for the early warning report template based on their recent inspection data.

[0026] The present invention also provides a sewage treatment optimization operation system, including: a daily inspection node setting module, an inspection cycle setting module, a health index calculation module, and an early warning report generation module;

[0027] Daily inspection node setting module, used to set daily inspection nodes for sewage treatment equipment;

[0028] Inspection cycle setting module, used to preset and adjust inspection cycles for daily inspection nodes;

[0029] The health index calculation module is used to calculate the health index of the daily inspection node based on the obtained inspection data and the water quality data of the sewage treatment equipment inlet and outlet;

[0030] The early warning report generation module is used to generate early warning reports for daily inspection nodes whose health index does not meet the standard.

[0031] A sewage treatment optimization operation system as described above, wherein the inspection cycle setting module includes: an inspection cycle preset submodule, a stability coefficient calculation submodule, and an inspection cycle adjustment submodule;

[0032] The inspection cycle preset submodule is used to preset a preliminary inspection cycle for daily inspection nodes;

[0033] The stability coefficient calculation submodule is used to calculate the current stability coefficient of the equipment according to the real-time operation data of the sewage treatment equipment;

[0034] The inspection cycle adjustment submodule is used to adjust the inspection cycle according to the current stability coefficient of the sewage treatment equipment.

[0035] A sewage treatment optimization operation system as described above, wherein the health index calculation module includes: an inspection data pulling submodule, a water quality data pulling submodule, and a health index return submodule;

[0036] The inspection data pulling submodule is used to pull the inspection data of daily inspection nodes according to the inspection cycle;

[0037] The water quality data extraction submodule is used to extract the water quality measurement data at the inlet and outlet of the sewage treatment equipment;

[0038] The health index return submodule is used to return the health index of the daily inspection node based on the pulled inspection data and water quality data.

[0039] In the above-mentioned sewage treatment optimization operation system, the early warning report generation module includes: a daily inspection node screening submodule, an early warning report template creation submodule, a content filling submodule, and an early warning report transmission submodule;

[0040] The daily inspection node screening submodule is used to screen out the daily inspection nodes whose health index does not meet the standard;

[0041] The early warning report template creation submodule is used to create a template framework for early warning reports;

[0042] The content filling submodule is used to fill the content of the early warning report template with the recent inspection data of the filtered daily inspection nodes;

[0043] The early warning report transmission submodule is used to send the generated early warning report to the operation and maintenance personnel.

[0044] The beneficial effects achieved by the present invention are as follows: accurate monitoring of sewage treatment equipment, remote completion of daily inspections of equipment, and saving of labor costs; dynamic adjustment of inspection cycles, automatic identification of equipment health modes, and generation of early warning reports. While solving the problem of untimely and inadequate equipment maintenance, it also reduces the workload of operation and maintenance personnel, allowing each inspection and maintenance of the operation and maintenance personnel to have a clear purpose. Through this series of operation and maintenance processes, the operating efficiency of sewage treatment equipment is improved, thereby achieving the purpose of optimizing the sewage treatment operation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 This is a flow chart of a sewage treatment optimization operation method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0047] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0048] Embodiment 1

[0049] like Figure 1 As shown, the first embodiment of the present invention provides a sewage treatment optimization operation method, including:

[0050] Step S10: Setting daily inspection nodes for the sewage treatment equipment based on historical fault records;

[0051] Historical fault records reflect the locations where sewage treatment equipment is prone to failure. These locations are called fault nodes. Daily inspection nodes are set at these fault nodes, and sensor equipment is installed and inspection parameters are set according to the cause of the failure, so as to achieve accurate monitoring of the sewage treatment equipment.

[0052] Step S20: Preset a preliminary inspection cycle, and remotely obtain the inspection data of the daily inspection nodes according to the inspection cycle;

[0053] The initial inspection cycle depends on the frequency of historical failures of the sewage treatment equipment. The higher the frequency of equipment failures within a fixed time, the shorter the inspection cycle should be. For example, if a device fails three times in a month, with an average interval of 7 days, then the inspection cycle should be at least shorter than the average interval. In order to ensure preventive measures, it is best to halve the inspection and perform a routine inspection on the equipment every 3.5 days, and then make adjustments based on actual conditions.

[0054] Step S30: Calculate the current stability coefficient of the equipment according to the real-time operation data of the sewage treatment equipment, and adjust the inspection cycle based on the stability coefficient of the equipment;

[0055] By comparing the real-time operation data of the sewage treatment equipment with the historical operation data, the current stability of the equipment can be inferred, and the level of stability determines the adjustment direction of the inspection cycle. Specifically:

[0056] Step S31: Regularly obtain real-time operation data of the sewage treatment equipment;

[0057] The real-time operating data of the sewage treatment equipment includes operating status parameters such as current, voltage, amplitude, and load. The acquired data is stored in data set A.

[0058] Step S32: inputting the real-time operation data of the sewage treatment equipment into the stability coefficient calculation function to obtain the current stability coefficient of the equipment;

[0059] The stability coefficient calculation function is expressed as: , where A is the real-time operation data set of the sewage treatment equipment, is the kth running status parameter value in data set A, is the mean value of the kth operating status parameter in the historical operating data of the sewage treatment equipment, represents the value of the kth operating status parameter in the i-th historical operating data of the sewage treatment equipment. is the influence weight of the kth operating status parameter on the stability of the sewage treatment equipment, k ranges from 1 to z, z is the length of the data set A, i ranges from 1 to n, and n is the total number of historical operating data of the sewage treatment equipment.

[0060] Step S33: adjusting the inspection cycle according to the current stability coefficient of the sewage treatment equipment;

[0061] A critical value is set for the stability coefficient to determine the adjustment direction of the inspection cycle. If the current stability coefficient of the sewage treatment equipment is lower than the critical value, the inspection cycle of the equipment will be increased by one step, that is, the adjustment direction is positive; if the current stability coefficient of the sewage treatment equipment is higher than the critical value, the inspection cycle of the equipment will be reduced by one step, that is, the adjustment direction is negative; the step value is determined according to the actual situation, and the critical value is obtained by multiplying the average of each peak value of the stability coefficient calculation function curve in the historical operation data by 80%.

[0062] Step S40: Calculate the health index of the daily inspection node based on the acquired inspection data and the water quality data of the sewage treatment equipment inlet and outlet;

[0063] In order to calculate the health index of daily inspection nodes, it is necessary to conduct a comprehensive analysis based on the inspection data and the water quality data at the equipment inlet and outlet. This process usually involves the monitoring of multiple water quality parameters, such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), suspended solids (SS), ammonia nitrogen (NH3-N), etc., which specifically includes the following sub-steps:

[0064] Step S41: Collecting water quality data at the inlet and outlet of the sewage treatment equipment;

[0065] By installing measuring devices at the outlet and inlet of the sewage treatment equipment and using repeaters to connect the data collected by the measuring devices to the transmission link of the inspection data, water quality data can be obtained remotely.

[0066] Step S42: Create a health index calculation function for daily inspection nodes, which specifically includes the following sub-steps:

[0067] Step S421: creating a water quality improvement index calculation formula based on the water quality parameters at the inlet and outlet of the sewage treatment equipment;

[0068] The water quality improvement index calculation formula is used to evaluate the working capacity of sewage treatment equipment based on the water quality data at the inlet and outlet of sewage treatment equipment, which is a key factor in equipment health considerations. The water quality improvement index calculation formula is expressed as:

[0069] ,in is the water quality improvement index of the equipment where the pth daily inspection node is located, Indicates the value of the jth water quality parameter at the water outlet of the equipment at the pth daily inspection node. Indicates the value of the jth water quality parameter at the water inlet of the equipment where the pth daily inspection node is located. is the processing weight coefficient of the equipment for the jth water quality parameter, j ranges from 1 to m, and m is the total number of water quality parameters.

[0070] Step S422: creating an inspection node scoring formula based on the inspection data of the sewage treatment equipment;

[0071] Inspection node scoring formula The scoring formula is expressed as:

[0072] ,in is the score of the pth daily inspection node, Indicates the number of nodes in the p-th daily inspection The value of the inspection parameter. Indicates that when the pth daily inspection node fails, The value of the inspection parameter. The value ranges from 1 to L, where L is the total number of inspection parameter items in the inspection data.

[0073] Step S433: integrating the water quality improvement index calculation formula and the inspection data scoring formula to form a health index calculation function;

[0074] The health index calculation function is expressed as: ,in The water quality improvement index of the equipment where the pth daily inspection node is located, is the score of the pth daily inspection node, It is an adjustable parameter used to control the impact of inspection data on the health index.

[0075] Step S43: passing the inspection data and water quality data into the health index calculation function, and returning the current health index of the daily inspection node;

[0076] The inspection data of each daily inspection node and the water quality data of the inlet and outlet of the sewage treatment equipment where it is located are respectively passed into the health index calculation function, and the health index of each daily inspection node is returned, and then these health indices are stored in the data set P.

[0077] Step S50: generating an early warning report for daily inspection nodes whose health index does not meet the standard;

[0078] The early warning report should display the geographical location of the daily inspection nodes and the recent inspection data to provide a certain reference value for operation and maintenance personnel. The generation of the early warning report specifically includes the following sub-steps:

[0079] Step S51: setting a threshold for the health index based on the historical inspection data of the daily inspection node;

[0080] This threshold is a health index calculated based on the inspection data and water quality data within one to two inspection cycles before each failure of the daily inspection node, and the average value is set as the threshold of the health index.

[0081] Step S52: creating a template for an early warning report;

[0082] First, the template reserves two items, warning location and health index, as subtitles. The main title is the number and name of the daily inspection node. The content is the recent inspection data of the daily inspection node. You can also set some custom fields to display more content, such as defining a growth trend field to calculate the change in each inspection data within different inspection cycles.

[0083] Step S53: extracting daily inspection nodes whose health index is lower than the threshold, and filling the early warning report template with content according to their recent inspection data;

[0084] The daily inspection nodes represented by the data items below the threshold in the query set P are then queried for the recent inspection data of the daily inspection nodes, and finally the corresponding content is filled according to the preset fields of the early warning report template.

[0085] Step S60: Notify the operation and maintenance personnel to rush to the site to perform maintenance and repair on the daily inspection nodes mentioned in the early warning report;

[0086] According to the content of the early warning report, operation and maintenance personnel can locate daily inspection nodes with potential risks and rush to the site to perform preventive operation and maintenance inspections to reduce the negative impact of sewage treatment equipment failure on the sewage treatment operation process, thereby optimizing the sewage treatment operation process.

[0087] Embodiment 2

[0088] Embodiment 2 of the present invention provides a sewage treatment optimization operation system, including: a daily inspection node setting module, an inspection cycle setting module, a health index calculation module, and an early warning report generation module;

[0089] (1) Daily inspection node setting module, used to set daily inspection nodes for sewage treatment equipment;

[0090] Historical fault records reflect the locations where sewage treatment equipment is prone to failure. These locations are called fault nodes. Daily inspection nodes are set at these fault nodes, and sensor equipment is installed and inspection parameters are set according to the cause of the failure, so as to achieve accurate monitoring of the sewage treatment equipment.

[0091] (2) Inspection cycle setting module, used to preset and adjust the inspection cycle for daily inspection nodes, including inspection cycle preset submodule, stability coefficient calculation submodule, and inspection cycle adjustment submodule;

[0092] 1. Inspection cycle preset submodule, used to preset a preliminary inspection cycle for daily inspection nodes;

[0093] The initial inspection cycle depends on the frequency of historical failures of the sewage treatment equipment. The higher the frequency of equipment failures within a fixed time, the shorter the inspection cycle should be. For example, if a device fails three times in a month, with an average interval of 7 days, then the inspection cycle should be at least shorter than the average interval. In order to ensure preventive measures, it is best to halve the inspection and perform a routine inspection on the equipment every 3.5 days, and then make adjustments based on actual conditions.

[0094] 2. The stability coefficient calculation submodule is used to calculate the current stability coefficient of the equipment based on the real-time operation data of the sewage treatment equipment;

[0095] First, the real-time operating data of the sewage treatment equipment is obtained regularly, including operating status parameters such as current, voltage, amplitude, and load, and the obtained data is stored in data set A; then the obtained data is input into the stability coefficient calculation function to return the current stability coefficient of the equipment.

[0096] The stability coefficient calculation function is expressed as: , where A is the real-time operation data set of the sewage treatment equipment, is the kth running status parameter value in data set A, is the mean value of the kth operating status parameter in the historical operating data of the sewage treatment equipment, represents the value of the kth operating status parameter in the i-th historical operating data of the sewage treatment equipment. is the influence weight of the kth operating status parameter on the stability of the sewage treatment equipment, k ranges from 1 to z, z is the length of the data set A, i ranges from 1 to n, and n is the total number of historical operating data of the sewage treatment equipment.

[0097] 3. Inspection cycle adjustment submodule, used to adjust the inspection cycle according to the current stability coefficient of the sewage treatment equipment;

[0098] A critical value is set for the stability coefficient to determine the adjustment direction of the inspection cycle. If the current stability coefficient of the sewage treatment equipment is lower than the critical value, the inspection cycle of the equipment will be increased by one step, that is, the adjustment direction is positive; if the current stability coefficient of the sewage treatment equipment is higher than the critical value, the inspection cycle of the equipment will be reduced by one step, that is, the adjustment direction is negative; the step value is determined according to the actual situation, and the critical value is obtained by multiplying the average of each peak value of the stability coefficient calculation function curve in the historical operation data by 80%.

[0099] (3) A health index calculation module, which is used to calculate the health index of daily inspection nodes based on the obtained inspection data and the water quality data at the inlet and outlet of the sewage treatment equipment, including an inspection data pulling submodule, a water quality data pulling submodule, and a health index return submodule;

[0100] 1. Inspection data pulling submodule, used to pull inspection data of daily inspection nodes according to the inspection cycle;

[0101] 2. Water quality data extraction submodule, used to extract water quality measurement data at the inlet and outlet of sewage treatment equipment;

[0102] By installing measuring devices at the outlet and inlet of the sewage treatment equipment and using repeaters to connect the data collected by the measuring devices to the transmission link of the inspection data, water quality data can be obtained remotely.

[0103] 3. The health index return submodule is used to return the health index of the daily inspection node based on the pulled inspection data and water quality data;

[0104] The inspection data of each daily inspection node and the water quality data of the inlet and outlet of the sewage treatment equipment where it is located are respectively passed into the health index calculation function, and the health index of each daily inspection node is returned. The creation of the health index calculation function includes the following steps:

[0105] I. Create a water quality improvement index calculation formula based on the water quality parameters at the inlet and outlet of the sewage treatment equipment;

[0106] The water quality improvement index calculation formula is used to evaluate the working capacity of sewage treatment equipment based on the water quality data at the inlet and outlet of sewage treatment equipment, which is a key factor in equipment health considerations. The water quality improvement index calculation formula is expressed as:

[0107] ,in is the water quality improvement index of the equipment where the pth daily inspection node is located, Indicates the value of the jth water quality parameter at the water outlet of the equipment at the pth daily inspection node. Indicates the value of the jth water quality parameter at the water inlet of the equipment where the pth daily inspection node is located. is the processing weight coefficient of the equipment for the jth water quality parameter, j ranges from 1 to m, and m is the total number of water quality parameters.

[0108] II. Create inspection node scoring formula based on the inspection data of sewage treatment equipment;

[0109] Inspection node scoring formula The scoring formula is expressed as:

[0110] ,in is the score of the pth daily inspection node, Indicates the number of nodes in the p-th daily inspection The value of the inspection parameter. Indicates that when the pth daily inspection node fails, The value of the inspection parameter. The value ranges from 1 to L, where L is the total number of inspection parameter items in the inspection data.

[0111] III. Integrate the water quality improvement index calculation formula and the inspection data scoring formula to form a health index calculation function;

[0112] The health index calculation function is expressed as: ,in The water quality improvement index of the equipment where the pth daily inspection node is located, is the score of the pth daily inspection node, It is an adjustable parameter used to control the impact of inspection data on the health index.

[0113] (4) Early warning report generation module, used to generate early warning reports for daily inspection nodes whose health index does not meet the standard, including daily inspection node screening submodule, early warning report template creation submodule, content filling submodule, and early warning report communication submodule;

[0114] 1. Daily inspection node screening submodule, used to screen out daily inspection nodes whose health index does not meet the standard;

[0115] A threshold is set for the health index based on the historical inspection data of the daily inspection nodes, and then the daily inspection nodes with a health index lower than the threshold are screened out. This threshold is the health index calculated based on the inspection data and water quality data in one to two inspection cycles before each failure of the daily inspection node. The average value is set as the threshold of the health index.

[0116] 2. Early warning report template creation submodule, used to create a template framework for early warning reports;

[0117] First, the template reserves two items, warning location and health index, as subtitles. The main title is the number and name of the daily inspection node. The content is the recent inspection data of the daily inspection node. You can also set some custom fields to display more content, such as defining a growth trend field to calculate the change in each inspection data within different inspection cycles.

[0118] 3. The content filling submodule is used to fill the content of the early warning report template with the recent inspection data of the filtered daily inspection nodes;

[0119] 4. Early warning report transmission submodule, used to send the generated early warning report to the operation and maintenance personnel;

[0120] After receiving the report, the operation and maintenance personnel can rush to the site to perform maintenance and repairs on the daily inspection nodes mentioned in the report. According to the content of the early warning report, the operation and maintenance personnel can locate the daily inspection nodes with potential risks and rush to the site to perform preventive operation and maintenance, thereby reducing the negative impact of sewage treatment equipment failure on the sewage treatment operation process, thereby optimizing the sewage treatment operation process.

[0121] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing operation of sewage treatment, characterized in that: The method comprises the following steps:

1. Set daily inspection nodes for sewage treatment equipment based on historical fault records; 2. Preset a preliminary inspection cycle and remotely obtain the inspection data of daily inspection nodes according to the inspection cycle; 3. Calculate the current stability coefficient of the equipment according to the real-time operation data of the sewage treatment equipment, and adjust the inspection cycle based on the stability coefficient of the equipment; wherein, the real-time operation data of the sewage treatment equipment is input into the stability coefficient calculation function to obtain the current stability coefficient of the equipment; the stability coefficient calculation function is: , where A is the real-time operation data set of the sewage treatment equipment, is the kth running status parameter value in data set A, is the mean value of the kth operating status parameter in the historical operating data of the sewage treatment equipment, represents the value of the kth operating status parameter in the i-th historical operating data of the sewage treatment equipment. is the influence weight of the kth operating status parameter on the stability of the sewage treatment equipment, k ranges from 1 to z, z is the length of the data set A, i ranges from 1 to n, and n is the total number of historical operating data of the sewage treatment equipment; 4. Calculate the health index of the daily inspection node based on the obtained inspection data and the water quality data of the sewage treatment equipment inlet and outlet; create a health index calculation function for the daily inspection node; pass the inspection data and water quality data into the health index calculation function, and return the current health index of the daily inspection node; Create a health index calculation function for daily inspection nodes, including the following sub-steps: The water quality improvement index calculation formula is created based on the water quality data at the inlet and outlet of the sewage treatment equipment: ,in is the water quality improvement index of the equipment where the pth daily inspection node is located, Indicates the value of the jth water quality data at the water outlet of the equipment at the pth daily inspection node. Indicates the value of the jth water quality data at the water inlet of the equipment at the pth daily inspection node. is the processing emphasis coefficient of the equipment on the j-th water quality data, j ranges from 1 to m, and m is the total number of water quality data items; Create an inspection node scoring formula based on the inspection data of sewage treatment equipment: ,in is the score of the pth daily inspection node, Indicates the number of nodes in the p-th daily inspection The value of the inspection data item. Indicates that when the pth daily inspection node fails, The value of the inspection data item. The value ranges from 1 to L, where L is the total number of inspection data in the inspection data; The water quality improvement index calculation formula and the inspection node scoring formula are combined to form a health index calculation function: ,in The water quality improvement index of the equipment where the pth daily inspection node is located, is the score of the pth daily inspection node, It is an adjustable parameter used to control the impact of inspection data on the health index; 5. Generate early warning reports for daily inspection nodes whose health index does not meet the standard; 6. Operation and maintenance personnel rush to the site to perform maintenance and repairs on the daily inspection nodes mentioned in the early warning report.

2. A sewage treatment optimization operation method according to claim 1, characterized in that: Generate an early warning report for daily inspection nodes whose health index does not meet the standard, which includes the following sub-steps: Set a threshold for the health index based on the historical inspection data of daily inspection nodes; Create a template for an early warning report; Extract daily inspection nodes whose health index is lower than the threshold, and fill in the content for the early warning report template based on their recent inspection data.

3. A sewage treatment optimization operation system, characterized in that: The system includes: a daily inspection node setting module, an inspection cycle setting module, a health index calculation module, and an early warning report generation module; Daily inspection node setting module, used to set daily inspection nodes for sewage treatment equipment; Inspection cycle setting module, used to preset and adjust inspection cycles for daily inspection nodes; The health index calculation module is used to calculate the health index of the daily inspection node based on the obtained inspection data and the water quality data of the sewage treatment equipment inlet and outlet; The early warning report generation module is used to generate early warning reports for daily inspection nodes whose health index does not meet the standard; The inspection cycle setting module includes: an inspection cycle preset submodule, a stability coefficient calculation submodule, and an inspection cycle adjustment submodule; The inspection cycle preset submodule is used to preset a preliminary inspection cycle for daily inspection nodes; The stability coefficient calculation submodule is used to input the real-time operation data of the sewage treatment equipment into the stability coefficient calculation function to obtain the current stability coefficient of the equipment; the stability coefficient calculation function is: , where A is the real-time operation data set of the sewage treatment equipment, is the kth running status parameter value in data set A, is the mean value of the kth operating status parameter in the historical operating data of the sewage treatment equipment, represents the value of the kth operating status parameter in the i-th historical operating data of the sewage treatment equipment. is the influence weight of the kth operating status parameter on the stability of the sewage treatment equipment, k ranges from 1 to z, z is the length of the data set A, i ranges from 1 to n, and n is the total number of historical operating data of the sewage treatment equipment; The inspection cycle adjustment submodule is used to adjust the inspection cycle according to the current stability coefficient of the sewage treatment equipment; The health index calculation module includes: inspection data pulling submodule, water quality data pulling submodule, and health index return submodule; The inspection data pulling submodule is used to pull the inspection data of daily inspection nodes according to the inspection cycle; The water quality data extraction submodule is used to extract the water quality data at the inlet and outlet of the sewage treatment equipment; The health index return submodule is used to return the health index of the daily inspection node based on the pulled inspection data and water quality data; The calculation of the health index of the daily inspection node includes: creating a health index calculation function of the daily inspection node; passing the inspection data and water quality data into the health index calculation function, and returning the current health index of the daily inspection node; Create a health index calculation function for daily inspection nodes, including the following sub-steps: The water quality improvement index calculation formula is created based on the water quality data at the inlet and outlet of the sewage treatment equipment: ,in is the water quality improvement index of the equipment where the pth daily inspection node is located, Indicates the value of the jth water quality data at the water outlet of the equipment at the pth daily inspection node. Indicates the value of the jth water quality data at the water inlet of the equipment at the pth daily inspection node. is the processing emphasis coefficient of the equipment on the j-th water quality data, j ranges from 1 to m, and m is the total number of water quality data items; Create an inspection node scoring formula based on the inspection data of sewage treatment equipment: ,in is the score of the pth daily inspection node, Indicates the number of nodes in the p-th daily inspection The value of the inspection data item. Indicates that when the pth daily inspection node fails, The value of the inspection data item. The value ranges from 1 to L, where L is the total number of inspection data in the inspection data; The water quality improvement index calculation formula and the inspection node scoring formula are combined to form a health index calculation function: ,in The water quality improvement index of the equipment where the pth daily inspection node is located, is the score of the pth daily inspection node, It is an adjustable parameter used to control the impact of inspection data on the health index.

4. A sewage treatment optimization operation system according to claim 3, characterized in that: The early warning report generation module includes: daily inspection node screening submodule, early warning report template creation submodule, content filling submodule, and early warning report communication submodule; The daily inspection node screening submodule is used to screen out the daily inspection nodes whose health index does not meet the standard; The early warning report template creation submodule is used to create a template framework for early warning reports; The content filling submodule is used to fill the content of the early warning report template with the recent inspection data of the filtered daily inspection nodes; The early warning report transmission submodule is used to send the generated early warning report to the operation and maintenance personnel.

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