An operation method of an intelligent urban resource management information system
By processing urban gas use data, eliminating outliers, filling missing data, screening gas use habits, and using curve fitting and deviation calculations, the randomness and systematic problems of urban gas use monitoring are solved, and the accuracy of gas use is achieved Monitoring and efficient provisioning.
Patent Information
- Application Number
- CN202410641015.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-22
- Publication Date
- 2024-12-13
- Estimated Expiration
- 2044-05-22
AI Technical Summary
The monitoring of urban gas consumption is affected by random and systematic factors, resulting in inaccurate resource allocation and difficult to achieve real-time and effective resource management.
By collecting gas usage data in urban areas, eliminating abnormal gas usage values, filling and screening data, using curve fitting and deviation calculations, determining common gas usage habits, eliminating casual and saving household data, calculating systematic errors, and realizing Accurate monitoring and allocation of gas volume.
It improves the accuracy of monitoring of urban gas consumption, reduces random errors, ensures the efficiency and accuracy of resource allocation, adapts to seasonal changes and changes in household bases, and reduces the risk of gas shortage.
Smart Images
Figure CN118396234B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a management information system, and in particular to an operation method of an intelligent urban resource management information system. Background Art
[0002] The shortage of electricity, water and gas in urban areas directly affects the lives of residents in the area. The usage of urban resources such as electricity, water and gas needs to be monitored in real time in order to make effective and timely resource allocation.
[0003] Taking urban gas consumption as an example, there are various factors affecting the use of gas in the area, which affect the accuracy of gas consumption monitoring. These factors include random factors such as gas pipe leakage and forgetting to close the gas valve, as well as systematic factors such as the specific gas usage habits of a small number of residents in the area (such as using more gas in a short period of time). These random and systematic factors pose great difficulties for real-time monitoring of gas consumption in the area, making it difficult to accurately allocate urban resources. Summary of the invention
[0004] The purpose of the present invention is to provide an operation method of an intelligent urban resource management information system, which can eliminate random and systematic gas consumption errors, thereby being able to more accurately and effectively monitor the gas consumption of urban areas, thereby allocating urban resources more efficiently and accurately.
[0005] To achieve the above object, the present invention provides the following technical solutions:
[0006] A method for operating an urban resource intelligent management information system comprises the following steps:
[0007] Collection steps: For a specific area of the city, the total gas consumption of the area in that month is collected M times at a fixed time interval every day, recorded as L(i), where i=0, 1, 2, 3, ..., and several daily data are taken as a data set, recorded as E(i), where i=1, 2, 3, ...;
[0008] Steps for setting abnormal gas usage value: The gas usage of the i-th time interval is TUse(i)=L(i)-L(i-1). Taking the Nth day as the boundary, add up the gas usage TUse(i) of each time interval in the 30 days before the Nth day, and divide the sum by 30*M to get the average gas usage averUse of each time interval in the past 30 days. Take 3 times averUse, that is, 3*averUse, as the abnormal gas usage value;
[0009] Data exclusion steps: Starting from the Nth day, compare the gas usage Use (i) of each time interval with the abnormal gas usage value 3*averUse one by one:
[0010] If TUse(i)≤3*averUse, the two data values L(i) and L(i-1) corresponding to TUse(i) are retained;
[0011] If TUse(i)>3*averUse, the data value L(i) corresponding to TUse(i) is excluded;
[0012] Data filling step: If the data contained in E(i) corresponding to a certain day is less than M, then the data of E(i) is filled as follows:
[0013] Search the data sequentially, and search for the first L(i) of multiple data values that are successively excluded, recorded as L(first):
[0014] Case 1: If the last collected data value L(i) of the day in which L(first) is located has been excluded, the previous data value L(i) of L(first) is taken as the end data value, recorded as L(end), and the fifteenth data value L(i) before L(end) is taken as the start data value, recorded as L(st), and the replacement value L(P)' is calculated as follows:
[0015] L(P)'=L(end)+P* , P is the bit sequence of multiple data values that are successively excluded;
[0016] Case 2: If the last collected data value L(i) of the day where L(first) is located is not excluded, then the first data value L(i) of the day where L(first) is located that is not excluded is used as the starting data value L(st), and the fifteenth data value from L(st) is used as the ending data value, recorded as L(end), and the replacement value L(S)' is calculated according to the following formula:
[0017] L(S)' = L(st) - S* , S is the reciprocal order of multiple data values that are successively excluded;
[0018] For case 1 / case 2, each time a replacement value L(P)' / L(S)' is calculated, the position of L(first) is shifted back by one, and the data is searched sequentially, and so on.
[0019] Gas consumption determination steps: The difference between the last collected gas consumption of the day and the first collected gas consumption of the day is DayUse (i). The DayUse (i) of multiple days in a specific period is added together to obtain the gas consumption TO of the specific period. If the gas consumption TO of the specific period is greater than the set value, it is determined that the gas consumption in the urban area is in a state of shortage. The system arranges to allocate gas from the urban area that is not in a state of shortage to the urban area that is in a state of shortage.
[0020] Specifically, after the data filling step, the following steps are included:
[0021] Data screening steps: record the average daily gas usage time of each household in the area within a certain period of time as The daily average gas consumption of each household in the area within a certain period of time is recorded as ,by It indicates the average daily gas usage time of a household in the area within a certain period of time. It represents the daily average gas consumption of a household in the area within a certain period of time. Ri represents the deviation of the daily average gas consumption of each household and the daily average gas consumption duration from the average value. The formula is as follows:
[0022] Ri=
[0023] by( , ) as the center and the maximum allowable deviation value Rmax as the radius, which is called the boundary circle C. / As the standard slope K, tangent lines with slope K are drawn on the upper and lower sides of the boundary circle C, which are called the upper boundary tangent line L1 and the lower boundary tangent line L2 respectively, and the data coordinates (Ui, Ti) below the lower boundary tangent line L2 are excluded.
[0024] Specifically, after the data screening step, the following steps are included:
[0025] Curve fitting steps: The data coordinates (Ui, Ti) that are not excluded are used for curve fitting using the least squares method to obtain the gas consumption-gas consumption duration fitting relationship curve Q:
[0026] U=KF*T+BF
[0027] Among them, KF is the slope of the gas consumption-gas consumption time fitting relationship curve Q, and BF is the intercept of the gas consumption-gas consumption time fitting relationship curve Q;
[0028] Specifically, after the curve fitting step, the following steps are included:
[0029] Deviation degree calculation steps: Dis(i) represents the distance between the i-th excluded data coordinate (Ui, Ti) above the upper boundary tangent L1 and the gas consumption-gas consumption time fitting relationship curve Q, and G represents the sum of the distances between each excluded data coordinate (Ui, Ti) and the gas consumption-gas consumption time fitting relationship curve Q. The formula is as follows:
[0030] G=
[0031] If G is greater than the set value, the degree of deviation is higher and the risk of gas shortage in the area is greater, so sufficient spare gas needs to be allocated to the area.
[0032] Compared with the prior art, the present invention has the following beneficial effects:
[0033] This method uses a data exclusion step to exclude data with gas consumption per unit time interval greater than three times the average gas consumption. This situation is generally caused by pipeline leakage or forgetting to close the valve, which is a random error caused by other factors and the error is excluded.
[0034] This method supplements the excluded data through a data filling step, and fully considers the change trends of the first fifteen data on the left end and the last fifteen data on the right end of the multiple data values that are continuously excluded to fill the left data and the right data of the multiple data values that are continuously excluded, so that the supplemented data conforms to seasonal changes, changes in household bases, etc.
[0035] The method excludes the gas consumption data of households with relatively casual and extensive gas consumption and households with relatively economical gas consumption through the data screening step, and obtains the gas consumption data of households with general gas consumption habits. The curve of household data with general gas consumption habits is fitted through the curve fitting step. Then, the gas consumption deviation degree of households with relatively casual and extensive gas consumption is calculated through the deviation degree calculation step, so as to calculate the systematic error size in the area, so as to guide the gas allocation between urban areas. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] 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 of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0037] Figure 1 The division of daily fixed time intervals by the operation method of this embodiment, and the corresponding data set;
[0038] Figure 2 A schematic diagram of the method for running the present embodiment to exclude and fill in the gas consumption data;
[0039] Figure 3 is the data set after the data is excluded;
[0040] Figure 4 A schematic diagram of demarcating the boundaries of household gas usage data in the operating method of this embodiment;
[0041] Figure 5A schematic diagram of defining a standard slope straight line for the operation method of this embodiment;
[0042] Figure 6 This is a schematic diagram of excluding household data in the operation method of this embodiment;
[0043] Figure 7 This is a schematic diagram of calculating the degree of deviation of data above the L1 line according to the operating method of this embodiment. DETAILED DESCRIPTION
[0044] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0045] A method for operating an intelligent urban resource management information system, characterized in that it comprises the following steps:
[0046] Collection steps: For a specific area in the city, the total gas consumption of the area for the month is collected M times at fixed time intervals every day, recorded as L(i), where i=0, 1, 2, 3, ..., and several daily data are taken as a data set, recorded as E(i), where i=1, 2, 3, ....
[0047] In this embodiment: Figure 1 , M is 5 times, and the 5 data of each day are taken as the data set.
[0048] Steps for setting abnormal gas usage value: The gas usage in the i-th time interval is TUse(i)=L(i)-L(i-1). Taking the Nth day as the boundary, add up the gas usage TUse(i) of each time interval in the 30 days before the Nth day, and divide the sum by 30*M to get the average gas usage averUse of each time period in the past 30 days. Take 3 times averUse, that is, 3*averUse, as the abnormal gas usage value.
[0049] In this embodiment: Figure 1 , for example, TUse(1)=L(1)-L(0), TUse(2)=L(2)-L(1), and so on. Taking the Nth day as the boundary means taking the starting time of the Nth day as the boundary, that is, the time at L(0).
[0050] Data exclusion steps: Starting from the Nth day, compare the gas usage Use (i) of each time interval with the abnormal gas usage value 3*averUse one by one:
[0051] If TUse(i)≤3*averUse, the two data values L(i) and L(i-1) corresponding to TUse(i) are retained;
[0052] If TUse(i)>3*averUse, the data value L(i) corresponding to TUse(i) is excluded.
[0053] In this embodiment: Figure 2 , L(7)~L(11), because the difference between each data value and the previous data value is greater than 3*averUse, L(7)~L(11) are excluded, and a cross “×” is marked in the figure to indicate exclusion.
[0054] Data filling step: If the data contained in E(i) corresponding to a certain day is less than M, then the data of E(i) is filled as follows:
[0055] Search the data sequentially, and search for the first L(i) of multiple data values that are successively excluded, recorded as L(first):
[0056] Case 1: If the last collected data value L(i) of the day in which L(first) is located has been excluded, the previous data value L(i) of L(first) is taken as the end data value, recorded as L(end), and the fifteenth data value L(i) before L(end) is taken as the start data value, recorded as L(st), and the replacement value L(P)' is calculated as follows:
[0057] L(P)'=L(end)+P* , P is the bit sequence of multiple data values that are successively excluded;
[0058] In this embodiment: Figure 3 , the data set E(2) on day N+1 has less than 5 data points, and data padding is required for E(2). Figure 2 , search for the first L(7) of the multiple data values that are successively excluded, record it as L(first). The last collected data value L(10) on the N+1 day where L(first) (i.e. L(7)) is located has been excluded, so the previous data value L(6) of L(first) (i.e. L(7)) is used as L(end), and the fifteenth data value L(i) before L(end) is used as L(st), and the formula L(P)'= L(end)+P* Calculate multiple replacement values L(P)', where P is the bit sequence of multiple data values that are successively excluded, with the bit sequence P of L(7) being 1, the bit sequence P of L(10) being 4, and so on.
[0059] Case 2: If the last collected data value L(i) of the day where L(first) is located is not excluded, then the first data value L(i) of the day where L(first) is located that is not excluded is used as the starting data value L(st), and the fifteenth data value from L(st) is used as the ending data value, recorded as L(end), and the replacement value L(S)' is calculated according to the following formula:
[0060] L(S)' = L(st) - S* , S is the reciprocal order of multiple data values that are successively excluded;
[0061] In this embodiment: Figure 3 , the data set E(3) on day N+2 has less than 5 data points, and data padding is required for E(3). Figure 2 , when L(11) is found, the difference between L(11) and L(10) exceeds 3*averUse, so L(11) is excluded. L(11) is L(first), and the last collected data value L(15) on the N+2 day where L(first) (i.e. L(11)) is located is not excluded. Then the first non-excluded data value L(12) on the N+2 day where (first) (i.e. L(11)) is located is used as the starting data value L(st), and the fifteenth data value from L(st) is used as the ending data value, recorded as L(end), according to the formula: L(S)'= L(st)- S * Calculate the replacement value L(S)', where S is the reciprocal order of multiple data values that are successively excluded, such as the reciprocal order of L(11) is 1.
[0062] For case 1 / case 2, each time a replacement value L(P)' / L(S)' is calculated, the position of L(first) is shifted back by one, and the data is searched sequentially, and this process repeats.
[0063] The operation of case 1 fills in the multiple data on the left side of the multiple data values that are successively excluded by referring to the pattern of the fifteen data before L(first)(L(7)). The operation of case 2 fills in the multiple data on the right side of the multiple data values that are successively excluded by referring to the pattern of the fifteen data after L(first)(L(11)), so that the reference value can be sought forward or backward in a more targeted manner to fill in the excluded data, making the filled data closer to reality.
[0064] Data screening steps: see Figure 5 , Figure 6 The average daily gas usage time of each household in the area within a certain period of time is recorded as The daily average gas consumption of each household in the area within a certain period of time is recorded as , Ti represents the average daily gas usage duration of a household in the area within a certain period of time, Ui represents the average daily gas consumption of a household in the area within a certain period of time, and Ri represents the deviation of the daily average gas consumption and the daily average gas usage duration of each household from the average value. The formula is as follows:
[0065] Ri=
[0066] by( , ) as the center and the maximum allowable deviation value Rmax as the radius, which is called the boundary circle C. / As the standard slope K, tangent lines with slope K are drawn on the upper and lower sides of the boundary circle C, which are called the upper boundary tangent line L1 and the lower boundary tangent line L2 respectively, and the data coordinates (Ui, Ti) below the lower boundary tangent line L2 are excluded.
[0067] The data coordinates (Ui, Ti) below the lower boundary tangent line L2 are the gas usage data of households with relatively restrained gas usage. The gas usage of these households is low and does not affect the gas shortage in the area, so they are excluded.
[0068] The data coordinates (Ui, Ti) above the lower boundary tangent line L1 are the gas usage data of households that use gas more casually and extensively. Such households can use up a large amount of gas in a shorter period of time, which has a crucial impact on the total amount of gas used in the area. Therefore, their data are retained for subsequent analysis.
[0069] Curve fitting steps: see Figure 7 , the data coordinates (Ui, Ti) that are not excluded are fitted using the least squares method to obtain the gas consumption-gas consumption time fitting relationship curve Q:
[0070] U=KF*T+BF
[0071] Among them, KF is the slope of the gas consumption-gas consumption time fitting relationship curve Q, and BF is the intercept of the gas consumption-gas consumption time fitting relationship curve Q;
[0072] Deviation degree calculation steps: Dis(i) represents the distance between the i-th excluded data coordinate (Ui, Ti) above the upper boundary tangent L1 and the gas consumption-gas consumption time fitting relationship curve Q, and G represents the sum of the distances between each excluded data coordinate (Ui, Ti) and the gas consumption-gas consumption time fitting relationship curve Q. The formula is as follows:
[0073] G=
[0074] If G(G= ) is greater than the set value, the degree of deviation is high, and the risk of gas shortage in the area is greater, and sufficient spare gas needs to be allocated to the area.
[0075] Gas consumption determination steps: The difference between the last collected gas consumption of the day and the first collected gas consumption of the day is DayUse (i). The DayUse (i) of multiple days in a specific period is added together to obtain the gas consumption TO of the specific period. If the gas consumption TO of the specific period is greater than the set value, it is determined that the gas consumption in the urban area is in a state of shortage. The system arranges to allocate gas from the urban area that is not in a state of shortage to the urban area that is in a state of shortage.
[0076] The technical effects of the operation method of the urban resource intelligent management information system of the present invention are as follows:
[0077] This method uses a data exclusion step to exclude data with gas consumption per unit time interval greater than three times the average gas consumption. This situation is generally caused by pipeline leakage or forgetting to close the valve, which is a random error caused by other factors and the error is excluded.
[0078] This method supplements the excluded data through a data filling step, and fully considers the change trends of the first fifteen data on the left end and the last fifteen data on the right end of the multiple data values that are continuously excluded to fill the left data and the right data of the multiple data values that are continuously excluded, so that the supplemented data conforms to seasonal changes, changes in household bases, etc.
[0079] The method excludes the gas consumption data of households with relatively casual and extensive gas consumption and households with relatively economical gas consumption through the data screening step, and obtains the gas consumption data of households with general gas consumption habits. The curve of household data with general gas consumption habits is fitted through the curve fitting step. Then, the gas consumption deviation degree of households with relatively casual and extensive gas consumption is calculated through the deviation degree calculation step, so as to calculate the systematic error size in the area, so as to guide the gas allocation between urban areas.
[0080] The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Although the present invention has been disclosed as above in the form of preferred embodiments, it is not intended to limit the present invention. Any technical personnel in the field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.
Claims
1. A method for operating an intelligent urban resource management information system, characterized in that: The following steps are involved: Collection steps: For a specific area of the city, the total gas consumption of the area in that month is collected M times at a fixed time interval every day, recorded as L(i), where i=0, 1, 2, 3, ..., and several daily data are taken as a data set, recorded as E(i), where i=1, 2, 3, ...; Steps for setting abnormal gas usage value: The gas usage of the i-th time interval is TUse(i)=L(i)-L(i-1). Taking the Nth day as the boundary, add up the gas usage TUse(i) of each time interval in the 30 days before the Nth day, and divide the sum by 30*M to get the average gas usage averUse of each time interval in the past 30 days. Take 3 times averUse, that is, 3*averUse, as the abnormal gas usage value; Data exclusion steps: Starting from the Nth day, compare the gas usage Use (i) of each time interval with the abnormal gas usage value 3*averUse one by one: If TUse(i)≤3*averUse, the two data values L(i) and L(i-1) corresponding to TUse(i) are retained; If TUse(i)>3*averUse, the data value L(i) corresponding to TUse(i) is excluded; Data filling step: If the data contained in E(i) corresponding to a certain day is less than M, then the data of E(i) is filled as follows: Search the data sequentially, and search for the first L(i) of multiple data values that are successively excluded, recorded as L(first): Case 1: If the last collected data value L(i) of the day in which L(first) is located has been excluded, the previous data value L(i) of L(first) is taken as the end data value, recorded as L(end), and the fifteenth data value L(i) before L(end) is taken as the start data value, recorded as L(st), and the replacement value L(P)' is calculated as follows: L(P)'=L(end)+P* , P is the bit sequence of multiple data values that are successively excluded; Case 2: If the last collected data value L(i) of the day where L(first) is located is not excluded, then the first data value L(i) of the day where L(first) is located that is not excluded is used as the starting data value L(st), and the fifteenth data value from L(st) is used as the ending data value, recorded as L(end), and the replacement value L(S)' is calculated according to the following formula: L(S)' = L(st) - S* , S is the reciprocal order of multiple data values that are successively excluded; For case 1 / case 2, each time a replacement value L(P)' / L(S)' is calculated, the position of L(first) is shifted back by one, and the data is searched in sequence, and so on. Data screening steps: record the average daily gas usage time of each household in the area within a certain period of time as The daily average gas consumption of each household in the area within a certain period of time is recorded as ,by It indicates the average daily gas usage time of a household in the area within a certain period of time. It represents the daily average gas consumption of a household in the area within a certain period of time. Ri represents the deviation of the daily average gas consumption of each household and the daily average gas consumption duration from the average value. The formula is as follows: Ri= by( , ) as the center and the maximum allowable deviation value Rmax as the radius, which is called the boundary circle C. / As the standard slope K, draw tangent lines with slope K on the upper and lower sides of the boundary circle C, which are called the upper boundary tangent line L1 and the lower boundary tangent line L2 respectively, and exclude the data coordinates (Ui, Ti) below the lower boundary tangent line L2; Curve fitting steps: The data coordinates (Ui, Ti) that are not excluded are used for curve fitting using the least squares method to obtain the gas consumption-gas consumption duration fitting relationship curve Q: U=KF*T+BF Among them, KF is the slope of the gas consumption-gas consumption time fitting relationship curve Q, and BF is the intercept of the gas consumption-gas consumption time fitting relationship curve Q; Deviation degree calculation steps: Dis(i) represents the distance between the i-th excluded data coordinate (Ui, Ti) above the upper boundary tangent L1 and the gas consumption-gas consumption time fitting relationship curve Q, and G represents the sum of the distances between each excluded data coordinate (Ui, Ti) and the gas consumption-gas consumption time fitting relationship curve Q. The formula is as follows: G= If G is greater than the set value, the deviation is high, and the risk of gas shortage in the area is greater, so sufficient spare gas needs to be allocated to the area; Gas consumption determination steps: The difference between the last collected gas consumption of the day and the first collected gas consumption of the day is DayUse (i). The DayUse (i) of multiple days in a specific period is added together to obtain the gas consumption TO of the specific period. If the gas consumption TO of the specific period is greater than the set value, it is determined that the gas consumption in the urban area is in a state of shortage. The system arranges to allocate gas from the urban area that is not in a state of shortage to the urban area that is in a state of shortage.