A green construction wastewater treatment analysis system and method based on big data

Abstract

The present invention discloses a green construction construction wastewater treatment analysis system and method based on big data, belonging to the field of big data wastewater treatment technology. The system includes a data acquisition module, a processing module and a comparison module; the data acquisition module is used to collect various initial data from construction wastewater and summarize them; the processing module is used to analyze the initial data collected by the data acquisition module and process the construction wastewater; the comparison module is used to compare and process the real-time monitoring data and segmented prediction data generated by the processing module; the output end of the data acquisition module is connected to the input end of the processing module; the output end of the processing module is connected to the input end of the comparison module; the present invention also provides a method for implementing the system, which can effectively improve the existing construction wastewater treatment technology, which has a large loss of manpower and material resources and cannot effectively discover and solve problems in a timely manner.

Description

Technical Field

[0001] The present invention relates to the technical field of big data wastewater treatment, and in particular to a green construction wastewater treatment analysis system and method based on big data. Background Art

[0002] Big data refers to a collection of data with large volume, multiple types and fast processing speed. It is usually accompanied by the use of data mining and other technologies to process and analyze data, obtain certain specific rules and expected values ​​from massive data, and thus bring convenience to people's lives.

[0003] In urban management, construction wastewater treatment is an important task. In recent years, with the continuous improvement of environmental protection requirements, the treatment and discharge standards of construction wastewater are also constantly increasing, but the treatment of construction wastewater is a very complicated process. At present, many construction wastewater treatment systems lack automated control and are highly dependent on manual labor. In addition, a large number of operators lack comprehensive and specific scientific background knowledge, resulting in low efficiency of wastewater treatment. In addition, many wastewater treatment systems do not have real-time monitoring functions, so they cannot promptly detect and deal with various problems in operation, and respond slowly, resulting in failure to meet wastewater treatment standards and waste of material and human resources. Summary of the invention

[0004] The purpose of the present invention is to provide a green construction wastewater treatment analysis system and method based on big data to alleviate the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a green construction wastewater treatment analysis system based on big data, the system comprising a data acquisition module, a processing module and a comparison module;

[0006] The data acquisition module is used to collect various initial data from construction wastewater and summarize them; the processing module is used to analyze the initial data collected by the data acquisition module and process the construction wastewater; the comparison module is used to compare and process the real-time monitoring data and segmented prediction data generated by the processing module;

[0007] The output end of the data acquisition module is connected to the input end of the processing module; the output end of the processing module is connected to the input end of the comparison module.

[0008] The data collection module includes an indicator selection unit and a data collection unit;

[0009] The index selection unit is used to determine the indexes for initial data collection in the process of construction wastewater treatment, and by comprehensively considering the cost and social environment factors, the indexes to be collected and the treatment weights of different pollutant indexes are determined according to the types and concentrations of pollutants in the wastewater; the data collection unit is used to collect and summarize the initial data of the determined indicators;

[0010] The output end of the indicator selection unit is connected to the input end of the data collection unit; the output end of the data collection unit is connected to the input end of the processing module.

[0011] The processing module includes a database unit, a data analysis unit, a wastewater treatment unit, a real-time monitoring unit and an m-segment prediction unit;

[0012] The database unit is used to store and retrieve the historical building wastewater treatment data information; the data analysis unit is used to analyze the database unit data and select similar data to the initial data collected by the data collection unit; the wastewater treatment unit is used to treat the wastewater based on the similar data information selected by the data analysis unit; the real-time monitoring unit is used to monitor the changes of various indicator data in the wastewater treatment process in real time; the m-segment prediction unit is used to predict the indicator data of the wastewater treatment process in sections with reference to the similar data information selected by the data analysis unit;

[0013] The output end of the database unit is connected to the input end of the data analysis unit; the output end of the data analysis unit is connected to the input end of the wastewater treatment unit and the m-segment prediction unit; the output end of the wastewater treatment unit is connected to the input end of the real-time monitoring unit; the output end of the real-time monitoring unit and the m-segment prediction unit is connected to the input end of the comparison module.

[0014] The comparison module includes a 1st-order error threshold unit, a 1st-order comparison unit, a 2nd-order error threshold unit, a 2nd-order comparison unit, ..., an m-order error threshold unit, an m-order comparison unit and a processing completion unit;

[0015] The 1st-order error threshold unit is used to set the theoretical error value between the first-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the 1st-order comparison unit is used to judge whether the actual error between the first-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the 2nd-order error threshold unit is used to set the theoretical error value between the second-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the 2nd-order comparison unit is used to judge whether the actual error between the second-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the m-order error threshold unit is used to set the theoretical error value between the mth-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the m-order comparison unit is used to judge whether the actual error between the mth-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the processing completion unit is used to integrate and store the data information generated during the wastewater treatment process;

[0016] The output end of the 1st-order error threshold unit is connected to the input end of the 1st-order comparison unit; the output end of the 1st-order comparison unit is connected to the input end of the data analysis unit and the 2nd-order error threshold unit; the output end of the m-order error threshold unit is connected to the input end of the m-order comparison unit; the output end of the m-order comparison unit is connected to the input end of the data analysis unit and the processing completion unit.

[0017] A green construction wastewater treatment analysis method based on big data, the method comprising the following steps:

[0018] S1. According to the types and concentrations of pollutants in construction wastewater, economic costs and emission standards, determine the indicators to be collected and set the treatment weights of different pollutant indicators, and use sensors to obtain the initial data of each pollutant indicator in the wastewater;

[0019] S2. Analyze the historical building wastewater treatment information according to the database information, obtain similar data information of the initial data obtained by the sensor in step S1, and process the building wastewater;

[0020] S3. Use sensors to monitor various indicator data in wastewater in real time to obtain real-time monitoring data, set segmented prediction data according to database information, set theoretical error thresholds between segmented prediction data and real-time monitoring data under emission standards, and compare whether the actual errors between segmented prediction data and real-time monitoring data meet the theoretical error thresholds;

[0021] S4, the error between the real-time monitoring data obtained in step S3 and the segmented prediction data is greater than the error threshold, returning to step S2, analyzing the historical building wastewater treatment information according to the database information, obtaining similar data information of the real-time monitoring data to treat the building wastewater;

[0022] S5. If the error between the real-time monitoring data and the segmented prediction data obtained in step S3 is not greater than the error threshold, the next stage of comparison between the prediction data and the real-time monitoring data is entered until the wastewater treatment is completed.

[0023] In step S1, the collected index a is determined according to the type and concentration of pollutants in the construction wastewater, the economic cost and the emission standard. The n index data can be expressed as a 1 、a 2 , …, a n-1 、a n , according to different pollutants, set the weight of each index p 1 、p 2 ,…,p n-1 、p n , there exists p 1 +p 2 +…+p n-1 +p n =100%, the initial data of the sensor index can be expressed as A 1 , A 2 , …, A n-1 , A n .

[0024] In step S2, the big data information analyzes the wastewater treatment information of the historical buildings to obtain similar data to the initial data obtained by the sensor in step S1. The similar data can be determined by the similarity s according to the formula:

[0025] s=|a 1i -A 1 |*p 1 +|a 2i -A 2 |*p 2 +…+|a ni -A n |*p n

[0026] Among them, a 1i 、a 2i , …, a ni represents the initial data of each indicator in the i-th group in the database. When the initial data of the corresponding indicator is completely consistent with the initial data obtained by the sensor, the s value is 0. Therefore, when the s value is the smallest, the initial data of this group of indicators is a 1i 、a 2i , …, a ni This is similar data.

[0027] In step S3, the theoretical error threshold can be obtained by the formula. If the following inequality holds, it means that the actual error satisfies the error threshold, otherwise it does not:

[0028]

[0029] 0≤d≤1, 1≤j≤n, 1≤k≤n, 1≤t≤n;

[0030] Among them, d is the error coefficient between real-time monitoring data and segmented prediction data, a j Represents the initial data of each indicator predicted at this stage, p t Represents the weight of each indicator, a k Represents the real-time monitoring data of each indicator in this stage, and represents the error threshold, Indicates the actual error.

[0031] In step S4, if the actual error value does not meet the above theoretical error threshold, the real-time monitoring data of this stage will replace the initial data obtained by the sensor and return to step S2. The historical building wastewater treatment information is analyzed according to the database information to obtain similar data information of the real-time monitoring data to treat the building wastewater; if it is satisfied, proceed to step S5 and enter the next stage to compare the predicted data with the real-time monitoring data until the wastewater treatment is completed.

[0032] Compared with the prior art, the beneficial effects achieved by the present invention are: big data technology can help managers understand the entire process of construction wastewater treatment from a detailed and macro perspective; big data technology enhances the visualization of the wastewater treatment process, which is more conducive to the supervision of environmental protection departments and the public; big data technology analysis can help operators discover problems in a timely manner, make auxiliary decisions, and improve wastewater treatment measures through summary and learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 It is a flow chart of a green construction wastewater treatment and analysis system based on big data of the present invention;

[0035] Figure 2 It is a schematic diagram of the steps of a green construction wastewater treatment analysis method based on big data of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1-Figure 2 ,The present invention provides a technical solution: a green construction wastewater treatment and analysis system based on big data, the system includes a data acquisition module, a processing module and a comparison module;

[0038] The data acquisition module is used to collect various initial data from construction wastewater and summarize them; the processing module is used to analyze the initial data collected by the data acquisition module and process the construction wastewater; the comparison module is used to compare and process the real-time monitoring data and segmented prediction data generated by the processing module;

[0039] The output end of the data acquisition module is connected to the input end of the processing module; the output end of the processing module is connected to the input end of the comparison module.

[0040] The data collection module includes an indicator selection unit and a data collection unit;

[0041] The index selection unit is used to determine the indexes for initial data collection in the process of construction wastewater treatment, and by comprehensively considering the cost and social environment factors, the indexes to be collected and the treatment weights of different pollutant indexes are determined according to the types and concentrations of pollutants in the wastewater; the data collection unit is used to collect and summarize the initial data of the determined indicators;

[0042] The output end of the indicator selection unit is connected to the input end of the data collection unit; the output end of the data collection unit is connected to the input end of the processing module.

[0043] The processing module includes a database unit, a data analysis unit, a wastewater treatment unit, a real-time monitoring unit and an m-segment prediction unit;

[0044] The database unit is used to store and retrieve the historical building wastewater treatment data information; the data analysis unit is used to analyze the database unit data and select similar data to the initial data collected by the data collection unit; the wastewater treatment unit is used to treat the wastewater based on the similar data information selected by the data analysis unit; the real-time monitoring unit is used to monitor the changes of various indicator data in the wastewater treatment process in real time; the m-segment prediction unit is used to predict the indicator data of the wastewater treatment process in sections with reference to the similar data information selected by the data analysis unit;

[0045] The output end of the database unit is connected to the input end of the data analysis unit; the output end of the data analysis unit is connected to the input end of the wastewater treatment unit and the m-segment prediction unit; the output end of the wastewater treatment unit is connected to the input end of the real-time monitoring unit; the output end of the real-time monitoring unit and the m-segment prediction unit is connected to the input end of the comparison module.

[0046] The comparison module includes a 1st-order error threshold unit, a 1st-order comparison unit, a 2nd-order error threshold unit, a 2nd-order comparison unit, ..., an m-order error threshold unit, an m-order comparison unit and a processing completion unit;

[0047] The 1st-order error threshold unit is used to set the theoretical error value between the first-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the 1st-order comparison unit is used to judge whether the actual error between the first-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the 2nd-order error threshold unit is used to set the theoretical error value between the second-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the 2nd-order comparison unit is used to judge whether the actual error between the second-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the m-order error threshold unit is used to set the theoretical error value between the mth-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the m-order comparison unit is used to judge whether the actual error between the mth-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the processing completion unit is used to integrate and store the data information generated during the wastewater treatment process;

[0048] The output end of the 1st-order error threshold unit is connected to the input end of the 1st-order comparison unit; the output end of the 1st-order comparison unit is connected to the input end of the data analysis unit and the 2nd-order error threshold unit; the output end of the m-order error threshold unit is connected to the input end of the m-order comparison unit; the output end of the m-order comparison unit is connected to the input end of the data analysis unit and the processing completion unit.

[0049] A green construction wastewater treatment analysis method based on big data, the method comprising the following steps:

[0050] S1. According to the types and concentrations of pollutants in construction wastewater, economic costs and emission standards, determine the indicators to be collected and set the treatment weights of different pollutant indicators, and use sensors to obtain the initial data of each pollutant indicator in the wastewater;

[0051] S2. Analyze the historical building wastewater treatment information according to the database information, obtain similar data information of the initial data obtained by the sensor in step S1, and process the building wastewater;

[0052] S3. Use sensors to monitor various indicator data in wastewater in real time to obtain real-time monitoring data, set segmented prediction data according to database information, set theoretical error thresholds between segmented prediction data and real-time monitoring data under emission standards, and compare whether the actual errors between segmented prediction data and real-time monitoring data meet the theoretical error thresholds;

[0053] S4, the error between the real-time monitoring data obtained in step S3 and the segmented prediction data is greater than the error threshold, returning to step S2, analyzing the historical building wastewater treatment information according to the database information, obtaining similar data information of the real-time monitoring data to treat the building wastewater;

[0054] S5. If the error between the real-time monitoring data and the segmented prediction data obtained in step S3 is not greater than the error threshold, the next stage of comparison between the prediction data and the real-time monitoring data is entered until the wastewater treatment is completed.

[0055] In step S1, the collected index a is determined according to the type and concentration of pollutants in the construction wastewater, the economic cost and the emission standard. The n index data can be expressed as a 1 、a 2 , …, a n-1 、a n , according to different pollutants, set the weight of each index p 1 、p 2 ,…,p n-1 、p n , there exists p 1 +p 2 +…+p n-1 +p n =100%, the initial data of the sensor index can be expressed as A 1 , A 2 , …, A n-1 , A n .

[0056] In step S2, the big data information analyzes the wastewater treatment information of the historical buildings to obtain similar data to the initial data obtained by the sensor in step S1. The similar data can be determined by the similarity s according to the formula:

[0057] s=|a 1i -A 1 |*p 1 +|a 2i -A 2 |*p 2 +…+|a ni -An |*p n

[0058] Among them, a 1i 、a 2i , …, a ni represents the initial data of each indicator in the i-th group in the database. When the initial data of the corresponding indicator is completely consistent with the initial data obtained by the sensor, the s value is 0. Therefore, when the s value is the smallest, the initial data of this group of indicators is a 1i 、a 2i , …, a ni This is similar data.

[0059] In step S3, the theoretical error threshold can be obtained by the formula. If the following inequality holds, it means that the actual error satisfies the error threshold, otherwise it does not:

[0060]

[0061] 0≤d≤1, 1≤j≤n, 1≤k≤n, 1≤t≤n;

[0062] Among them, d is the error coefficient between real-time monitoring data and segmented prediction data, a j Represents the initial data of each indicator predicted at this stage, p t Represents the weight of each indicator, a k Represents the real-time monitoring data of each indicator in this stage, and represents the error threshold, Indicates the actual error.

[0063] In step S4, if the actual error value does not meet the above theoretical error threshold, the real-time monitoring data of this stage will replace the initial data obtained by the sensor and return to step S2. The historical building wastewater treatment information is analyzed according to the database information to obtain similar data information of the real-time monitoring data to treat the building wastewater; if it is satisfied, proceed to step S5 and enter the next stage to compare the predicted data with the real-time monitoring data until the wastewater treatment is completed.

[0064] In this embodiment: according to the analysis of the local construction wastewater discharge standard, the indicators to be collected are determined to be pH value, ammonia nitrogen concentration and suspended solids concentration. The corresponding proportions are set to 35%, 35% and 30% according to the priority. The discharge standard is that the pH value range is 6 to 9, the ammonia nitrogen concentration is not more than 15 mg / L, and the suspended solids concentration is not more than 20 mg / L. The existing database data is as follows:

[0065] A set of data: pH value is 4.1, ammonia nitrogen concentration is 39mg / L, and suspended solids concentration is 46mg / L;

[0066] Stage 1: pH value is 5.7, ammonia nitrogen concentration is 27 mg / L, and suspended solids concentration is 30.8 mg / L;

[0067] Stage 2: pH value is 6.8, ammonia nitrogen concentration is 13 mg / L, and suspended solids concentration is 20 mg / L;

[0068] The second set of data: pH value is 4.7, ammonia nitrogen concentration is 30.2 mg / L, and suspended solids concentration is 40.8 mg / L;

[0069] Stage 1: pH value is 5.6, ammonia nitrogen concentration is 19.6 mg / L, and suspended solids concentration is 32.4 mg / L;

[0070] Stage 2: pH value is 6.4, ammonia nitrogen concentration is 14.7 mg / L, and suspended solids concentration is 18.3 mg / L;

[0071] Three sets of data: pH value is 5.3, ammonia nitrogen concentration is 32 mg / L, and suspended solids concentration is 33.6 mg / L;

[0072] Stage 1: pH value is 5.9, ammonia nitrogen concentration is 25 mg / L, and suspended solids concentration is 22.6 mg / L;

[0073] Stage 2: pH value is 6.4, ammonia nitrogen concentration is 14.3 mg / L, and suspended solids concentration is 17.6 mg / L;

[0074] The sensor obtains the following indicator data: pH value is 4.4, ammonia nitrogen concentration is 37 mg / L, and suspended solids concentration is 44.5 mg / L. Similar data are calculated according to the formula:

[0075] s=|a 1i -A 1 |*p 1 +|a 2i -A 2 |*p 2 +…+|a ni -A n |*p n

[0076] One group: s = |4.4-4.1|*35%+|37-39|*35%+|44.5-46|*30%=1.255;

[0077] Group 2: s = |4.4-4.7|*35% + |37-30.2|*35% + |44.5-40.8|*30% = 3.595;

[0078] Group 3: s = |4.4-5.3|*35% + |37-32|*35% + |44.5-33.6|*30% = 5.335;

[0079] The similar data are grouped together, and the target wastewater is treated according to the treatment method of the group of data;

[0080] The real-time monitoring data for the first stage are: pH value is 5.2, ammonia nitrogen concentration is 30 mg / L, and suspended solids concentration is 35 mg / L; the error coefficient is set to 5%, and the error threshold is calculated based on the formula and a set of first-stage data:

[0081]

[0082] It can be seen that the real-time monitoring data of the first stage does not meet the error threshold of the predicted data of the first stage, so the real-time monitoring data of the first stage replaces the data obtained by the initial sensor to obtain similar data for wastewater treatment:

[0083] One group: s = |5.2-4.1|*35%+|30-39|*35%+|35-46|*30%=6.835;

[0084] Group 2: s = |5.2-4.7|*35% + |30-30.2|*35% + |35-40.8|*30% = 1.985;

[0085] Group 3: s = |5.2-5.3|*35% + |30-32|*35% + |35-33.6|*30% = 1.155;

[0086] The similar data were divided into three groups, and the target wastewater was treated according to the three groups of data processing methods;

[0087] The real-time monitoring data for the new phase are: pH value is 5.8, ammonia nitrogen concentration is 24.3 mg / L, suspended solids concentration is 23.7 mg / L; the error coefficient is 5%, and the error threshold is calculated based on the formula and three sets of first-phase data:

[0088]

[0089] The real-time monitoring data of the new stage meets the error threshold of the predicted data of the first stage, and the next stage is carried out and compared. The real-time monitoring data of the new second stage is pH value 6.5, ammonia nitrogen concentration 14mg / L, suspended solids concentration 17.5mg / L; the error coefficient is 5%, and the error threshold is calculated according to the formula and three sets of second-stage data:

[0090]

[0091] The new second-stage real-time monitoring data meets the second-stage prediction data error threshold, and the wastewater treatment is completed and meets the emission standards.

[0092] 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.

[0093] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A green construction wastewater treatment analysis method based on big data, characterized by: The method comprises the following steps: S1. According to the types and concentrations of pollutants in construction wastewater, economic costs and emission standards, determine the indicators to be collected and set the treatment weights of different pollutant indicators, and use sensors to obtain the initial data of each pollutant indicator in the wastewater; S2. Analyze the historical building wastewater treatment information according to the database information, obtain similar data information of the initial data obtained by the sensor in step S1, and process the building wastewater; S3. Use sensors to monitor various indicator data in wastewater in real time to obtain real-time monitoring data, set segmented prediction data according to database information, set theoretical error thresholds between segmented prediction data and real-time monitoring data under emission standards, and compare whether the actual errors between segmented prediction data and real-time monitoring data meet the theoretical error thresholds; S4, the error between the real-time monitoring data obtained in step S3 and the segmented prediction data is greater than the error threshold, returning to step S2, analyzing the historical building wastewater treatment information according to the database information, obtaining similar data information of the real-time monitoring data to treat the building wastewater; S5, if the error between the real-time monitoring data obtained in step S3 and the segmented prediction data is not greater than the error threshold, the next stage of comparison between the prediction data and the real-time monitoring data is entered until the wastewater treatment is completed; In step S1, the collected indicators are determined according to the types and concentrations of pollutants in the construction wastewater, economic costs and emission standards. , n indicator data can be expressed as , , , , , according to different pollutants, set the weight of each indicator ,exist , the initial data of the sensor indicator can be expressed as ; In step S2, the big data information analyzes the wastewater treatment information of the historical buildings to obtain similar data to the initial data obtained by the sensor in step S1. Determine, according to the formula: ； in, , , , Represents the database The initial data of each indicator in the group is exactly the same as the initial data obtained by the sensor. The value is 0, so when the s value is the smallest, the initial data of this group of indicators , , , This is similar data.

2. According to a big data-based green construction wastewater treatment analysis method according to claim 1, it is characterized by: In step S3, the theoretical error threshold can be obtained by the formula. If the following inequality holds, it means that the actual error satisfies the error threshold, otherwise it does not: ； 0 1,1 I n,1 n,1 n; in, is the error coefficient between real-time monitoring data and segmented prediction data, Represents the initial data of each indicator predicted in this stage, Represents the weight of each indicator. Represents the real-time monitoring data of each indicator in this stage, represents the error threshold, Indicates the actual error.

3. The green construction wastewater treatment analysis method based on big data according to claim 2 is characterized by: In step S4, if the actual error value does not meet the above theoretical error threshold, the real-time monitoring data of this stage will replace the initial data obtained by the sensor and return to step S2. The historical building wastewater treatment information is analyzed according to the database information to obtain similar data information of the real-time monitoring data to treat the building wastewater; if it is satisfied, proceed to step S5 and enter the next stage to compare the predicted data with the real-time monitoring data until the wastewater treatment is completed.

4. A green construction wastewater treatment analysis system based on big data, using a green construction wastewater treatment analysis method based on big data as claimed in claim 1, characterized in that: The system includes a data acquisition module, a processing module and a comparison module; The data acquisition module is used to collect various initial data from construction wastewater and summarize them; the processing module is used to analyze the initial data collected by the data acquisition module and process the construction wastewater; the comparison module is used to compare and process the real-time monitoring data and segmented prediction data generated by the processing module; The output end of the data acquisition module is connected to the input end of the processing module; the output end of the processing module is connected to the input end of the comparison module.

5. The green construction wastewater treatment analysis system based on big data according to claim 4 is characterized by: The data collection module includes an indicator selection unit and a data collection unit; The index selection unit is used to determine the indexes for initial data collection in the process of construction wastewater treatment, and by comprehensively considering the cost and social environment factors, the indexes to be collected and the treatment weights of different pollutant indexes are determined according to the types and concentrations of pollutants in the wastewater; the data collection unit is used to collect and summarize the initial data of the determined indicators; The output end of the indicator selection unit is connected to the input end of the data collection unit; the output end of the data collection unit is connected to the input end of the processing module.

6. The green construction wastewater treatment analysis system based on big data according to claim 5 is characterized by: The processing module includes a database unit, a data analysis unit, a wastewater treatment unit, a real-time monitoring unit and an m-segment prediction unit; The database unit is used to store and retrieve historical building wastewater treatment data information; the data analysis unit is used to analyze the database unit data and select data similar to the initial data collected by the data collection unit; The wastewater treatment unit is used to process wastewater based on the similar data information selected by the data analysis unit; The real-time monitoring unit is used to monitor the changes of various index data in the wastewater treatment process in real time; the m-segment prediction unit is used to select similar data information from the data analysis unit to predict the index data of the wastewater treatment process in segments; The output end of the database unit is connected to the input end of the data analysis unit; the output end of the data analysis unit is connected to the input end of the wastewater treatment unit and the m-segment prediction unit; the output end of the wastewater treatment unit is connected to the input end of the real-time monitoring unit; the output end of the real-time monitoring unit and the m-segment prediction unit is connected to the input end of the comparison module.

7. The green construction wastewater treatment analysis system based on big data according to claim 6 is characterized by: The comparison module includes a 1st order error threshold unit, a 1st order comparison unit, a 2nd order error threshold unit, a 2nd order comparison unit, , an m-order error threshold unit, an m-order comparison unit and a processing completion unit; The 1st-order error threshold unit is used to set the theoretical error value between the first-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the 1st-order comparison unit is used to judge whether the actual error between the first-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the 2nd-order error threshold unit is used to set the theoretical error value between the second-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the 2nd-order comparison unit is used to judge whether the actual error between the second-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the m-order error threshold unit is used to set the theoretical error value between the mth-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit under the emission standard; the m-order comparison unit is used to judge whether the actual error between the mth-segment prediction data of the m-segment prediction unit and the real-time monitoring data of the real-time monitoring unit meets the theoretical error value; the processing completion unit is used to integrate and store the data information generated during the wastewater treatment process; The output end of the first-order error threshold unit is connected to the input end of the first-order comparison unit; The output end of the 1st order comparison unit is connected to the input end of the data analysis unit and the 2nd order error threshold unit; the output end of the mth order error threshold unit is connected to the input end of the mth order comparison unit; The output end of the m-order comparison unit is connected to the input ends of the data analysis unit and the processing completion unit.

Images