CN113822587B - Factory capacity evaluation method based on bus current data - Google Patents
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Abstract
The invention relates to a factory capacity evaluation method based on bus current data, which comprises the following steps: step S1, collecting factory power supply at regular timeInstantaneous current data of the bus; s2, selecting all current value data collected within N days before the current as samples, and calculating the current sum of each day; step S3, counting the probability distribution density p of the current sum of the first N daysi(ii) a Step S4, for piFitting a least square normal distribution model to obtain an evaluation model M; step S5, calculating a confidence interval of M; and step S6, evaluating the current and value of the current and the confidence interval of M on the current. The invention can be used for real-time effective evaluation of factory capacity, has convenient data acquisition, low cost and wide applicability, can carry out scientific and reasonable capacity evaluation according to historical data and normal distribution statistical theory, can update the evaluation model in real time along with the time, and realizes dynamic real-time tracking and accurate evaluation of the capacity.
Description
Technical Field
The invention belongs to the technical field of plant capacity monitoring and evaluation, and particularly relates to a plant capacity evaluation method based on bus current data.
Background
With the rapid development of the internet of things technology and the information technology, an effective information technology platform and a management tool are established, and the trends of real-time monitoring of enterprise operation conditions and systematization of post-loan risk management work are realized.
The daily actual capacity condition of a factory workshop of a production type enterprise can effectively reflect the operation condition of the enterprise, and in the prior art, various data related to the factory capacity are mainly acquired by a sensor or a manual mode, and capacity evaluation and prediction are realized by combining a machine learning method. For example, machine vision is used to detect good products and defective products in products and perform data statistics, and a deep learning network is combined to achieve capacity evaluation prediction (CN 202010442738.9). Mass data such as power consumption unit consumption, equipment operation, process instruments and the like are used as information sources, and a machine learning method is used for realizing capacity prediction (CN 201911224534.1). The factors influencing the productivity, such as factory building factors, personnel factors, equipment factors, material factors, the number of personnel on an operation post, the equipment loss rate and the like, are used as evaluation basis, and the productivity evaluation prediction is realized by combining a neural network (CN 202010774588.1).
The above prior art requires more sensors to be arranged due to more statistical factors, and some factors also require manual intervention. Meanwhile, due to more statistical factors, the productivity assessment method is often more complex. And needs to be customized, and the method has poor universality.
The invention provides a factory capacity evaluation method based on bus current, which aims to solve the problems that the traditional method cannot realize real-time monitoring, and the existing method needs high manpower and material resources, is complex in method and is not wide in adaptability.
Disclosure of Invention
Aiming at the defects in the existing plant capacity monitoring technology, the invention provides a plant capacity evaluation method based on bus current data. The method comprises the steps of firstly collecting current by using a current caliper sensor, summing the current to be used as a parameter index of the current of the same day, and counting the current distribution of a period of time; then, carrying out normal distribution fitting on the current distribution by using a least square method; then judging the working state of the current day through the division of confidence intervals; and finally, judging the production condition of the factory according to the current data monitored in the same day.
Compared with the prior art, the method can be used for effectively evaluating the factory capacity in real time, and is convenient in data acquisition, low in cost and wide in applicability. Scientific and reasonable capacity evaluation can be performed according to historical data and a normal distribution model statistical theory. The evaluation model can be updated in real time along with the time, and dynamic real-time tracking and accurate evaluation of the production capacity are realized.
The invention realizes the purpose through the following technical scheme:
a factory capacity evaluation method based on bus current data comprises the following steps:
step S1, collecting instantaneous current data of the factory power supply bus at regular time, and calculating an average value i (k) of the instantaneous current data meeting preset conditions, where k is 1, 2, …, m, and using the average value i (k) as current value data of the current collection;
step S2, selecting all current value data collected in N days before the current day as samples, and calculating current and data i (h) ═ Σ i (k) of each day in the previous N days, h ═ 1, 2, …, N;
step S3, selecting the maximum value Imax (h) and the minimum value Imin (h) from all the current and data I (h) in step S2, dividing K intervals between the maximum value Imax (h) and the minimum value Imin (h), and calculating the current of each day in the previous N days and the probability value p of each intervaliN (j)/N, (i ═ 1, 2, …, K), N (j), j ═ 1, 2, …, K total intervals, N (j), j ═ 1, 2, …, K is the current and i (h) falls in the number of intervals;
step S4, based on the probability value p obtained in step S3iConstruction of Normal distribution model M (mu, sigma)2)；
Step S5, setting confidence level value and calculating confidence interval lower limit IminU-2 σ and confidence interval upper bound Imax＝u+2σ；
Step S6, the current and data I collected and calculated on the same day and the confidence interval lower limit I calculated in the step S5minU-2 σ and upper confidence interval limit ImaxCarrying out comparison analysis when the I is less than IminJudging that the current day is in a low-capacity state, and judging that the current day is in a low-capacity state when I is larger than ImaxWhen the current day is high-yield, when Imin≤I≤ImaxThe current day is judged to be in a normal capacity state.
As a further optimization scheme of the present invention, the preset condition in step S1 is that the sampling rate of the instantaneous current data is fsThe acquisition time is T.
As a further optimization of the present invention, step S4 is based on probability value piConstruction of Normal distribution model M (mu, sigma)2) Then, the probability value p is fitted by the least square methodiTo obtain a normal distribution model M (mu, sigma)2)。
As a further optimization scheme of the present invention, the step S4 is to construct a normal distribution model M (mu, sigma)2) The method comprises the following specific steps:
step S4.1, new abscissa x ═ I with the midpoint of K intervals in step S3min(h)+(Imax(h)-Imin(h))/(2*K),Imin(h)+3*(Imax(h)-Imin(h))/(2*K),…,Imin(h)+(2*K-1)(Imax(h)-Imin(h) /(2 x K) } and the probability value p obtained in step S3 is comparediIs divided by (I)max(h)-Imin(h) K) as new ordinate y;
and S4.2, establishing objective function solving parameters and fitting to obtain a normal distribution model with the mean value u and the variance sigma by taking the new coordinates (x, y) as observed quantity data.
As a further optimization scheme of the present invention, the objective function formula is as follows:
y＝f(x,w)
wherein x is [ x ]1，x2，…，xn]T∈Rn，y＝R，w＝[w1，w2，…wm]TIs a matrix of undetermined parameters, and n>m, when the objective function takes the minimum value, corresponding to w ═ w1，w2，…wm]TThat is, the parameters are obtained, a normal distribution model with the mean value u and the variance sigma is obtained through fitting, n represents the number of observation samples, m represents the number of parameters in a fitting function, R represents a one-dimensional real number, and R representsnAnd the real number is expressed in n dimensions, y is an observed value, and f (x, w) is a fitting function value corresponding to the fitting function relation.
As a further optimization of the invention, the confidence level value pcThe setting was 95%.
As a further optimized solution of the present invention, in step S1, instantaneous current data of the plant power supply bus is collected at regular time, specifically, data collection is performed by hanging a current caliper sensor on the plant power supply bus.
The invention has the beneficial effects that:
1) the invention has convenient data acquisition, does not need equipment modification to install various sensors, only needs to acquire bus current, and adopts current calipers to realize non-contact installation;
2) the method has strong universality, only needs to collect the bus current of a factory workshop, has low technical implementation difficulty, and is suitable for various automatic production type enterprises;
3) the invention has low cost and convenient maintenance. Only the bus current needs to be acquired, so that the number of data acquisition terminals is small, the cost is low, and the later maintenance and replacement are convenient;
4) compared with the traditional method depending on the enterprise business report, the method has the advantages of good real-time performance, high informatization degree and small consumed manpower and material resources;
5) according to the invention, scientific and reasonable capacity evaluation is carried out according to the statistical theory of the normal distribution model, and compared with a machine learning method, the evaluation model has strong interpretability;
6) the evaluation model can be updated in real time along with the continuous update of data, dynamic real-time tracking and accurate evaluation of production capacity are realized, and sudden business condition changes caused by sudden increase or decrease of enterprise orders can be better adapted.
Drawings
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a line graph showing the instantaneous change of the current sampled by the factory 48 times a day in an embodiment of the present invention;
FIG. 3 is a graph of current and statistics for 154 days prior to the factory in an embodiment of the invention;
FIG. 4 is a graph of current and probability distribution over various intervals in an embodiment of the present invention;
FIG. 5 is a diagram of a normal distribution evaluation model in an embodiment of the present invention;
FIG. 6 is a schematic diagram of a capacity estimation model according to an embodiment of the present invention;
FIG. 7 is a model update rendering of the present invention;
fig. 8 is an evaluation result of an embodiment of the present invention.
Detailed Description
The present application will now be described in further detail with reference to the drawings, it should be noted that the following detailed description is given for illustrative purposes only and is not to be construed as limiting the scope of the present application, as those skilled in the art will be able to make numerous insubstantial modifications and adaptations to the present application based on the above disclosure.
Example 1
As shown in fig. 1 to 8, a method for evaluating plant capacity based on bus current data includes the following steps:
step S1, collecting instantaneous current data of the factory power supply bus at regular time, and calculating an average value i (k) of the instantaneous current data meeting preset conditions, where k is 1, 2, …, m, and using the average value i (k) as current value data of the current collection; in the step S1, acquiring instantaneous current data of the factory power supply bus at regular time, specifically, acquiring data by hanging a current caliper sensor on the factory power supply bus; the preset condition in step S1 is that the sampling rate of instantaneous current data is fsAnd the acquisition time is T.
Hanging a caliper current sensor on a factory bus, collecting the current sensor once every half hour and calculating the average value of the current every time by using the sensor, and collecting forty-eight times of current values in one day, wherein the current change condition of one day is shown in figure 2;
step S2, selecting all current value data collected in N days before the current day as samples, and calculating current and data i (h) ═ Σ i (k) of each day in the previous N days, h ═ 1, 2, …, N;
counting the current of each day and obtaining the current sum, wherein the current sum of the current of the;
step S3, selecting the maximum value Imax (h) and the minimum value Imin (h) from all the current and data I (h) in step S2, dividing K intervals between the maximum value Imax (h) and the minimum value Imin (h), and calculating the current of each day in the previous N days and the probability value p of each intervali＝n(j)/N，(i＝1，2，…，K)，n(j)，j＝1，2，…，K；
The electricity of the first 154 daysUniformly dividing 20 intervals between the maximum value and the minimum value in the flow sum, counting the current sum I (h) and the probability p of falling in each intervaliN (j)/N, (i ═ 1, 2, …, 20), where N (j), j ═ 1, 2, …, 20 are the number of current sums i (h) falling in each interval, and a profile of the current sum is plotted, as shown in fig. 4;
calculating the corresponding probability pi(i ═ 1, 2, …, 20), the specific steps of plotting the distribution of the current sum are as follows:
counting the current and data of 154 days before the factory, and using the number of days n falling in each intervali(i-1, 2, …, 20) divided by 154 days, i.e. the probability p that the current falls in each intervali(i＝1，2，…，20)；
Probability p of falling in each interval with the sum of currents as abscissai(i ═ 1, 2, …, 20) the current sum distribution is plotted on the ordinate;
step S4, based on the probability value p obtained in step S3iConstruction of Normal distribution model M (mu, sigma)2) (ii) a In step S4, probability value p is usediConstruction of Normal distribution model M (mu, sigma)2) Then, the probability value p is fitted by the least square methodiTo obtain a normal distribution model M (mu, sigma)2)。
Normal distribution model M (mu, sigma) is constructed in step S42) The method comprises the following specific steps:
step S4.1, new abscissa x ═ I with the midpoint of K intervals in step S3min(h)+(Imax(h)-Imin(h))/(2*K),Imin(h)+3*(Imax(h)-Imin(h))/(2*K),…,Imin(h)+(2*K-1)(Ima x(h)-Imin(h) /(2 x K) } and the probability value p obtained in step S3 is comparediIs divided by (I)max(h)-Imin(h) K) is taken as a new ordinate y, and the enclosed area of the final fitting curve and the abscissa is ensured to be 1;
and S4.2, establishing objective function solving parameters and fitting to obtain a normal distribution model with the mean value u and the variance sigma by taking the new coordinates (x, y) as observed quantity data.
The objective function is formulated as follows:
y＝f(x,w)
wherein x is [ x ]1，x2，…，xn]T∈Rn，y＝R，w＝[w1，w2，…wm]TIs a matrix of parameters to be determined, and n>m, when the objective function takes the minimum value, corresponding to w ═ w1，w2，…wm]TThat is, the parameters are obtained, a normal distribution model with the mean value u and the variance sigma is obtained through fitting, n represents the number of observation samples, m represents the number of parameters in a fitting function, R represents a one-dimensional real number, and R representsnRepresenting n-dimensional real numbers, y is an observed value, and f (x, w) is a fitting function value corresponding to the fitting function relationship; as shown in fig. 5, fitting results in a model of normal distribution;
specifically, the midpoint of 20 small intervals is taken as a new abscissa x, the probability of the ordinate is divided by the length of each interval to be taken as a new ordinate y, and the area enclosed by the finally-fitted normal distribution curve and the abscissa is ensured to be 1;
the new coordinates (x, y) are taken as observed quantities, and x ═ x1，x2，…，x20]T∈R20And y-R satisfies the following functional relationship:
y＝f(x,w)
wherein w ═ u, σ]TAnd u is a mean value and sigma is a variance for the undetermined parameter. To find the optimal estimate of the parameter w of the function f (x, w), 20 sets of observations (x, w) were madei，yi) (i ═ 1, 2, …, 20) solving an objective function:
when the objective function obtains the minimum value, u is 416.0, and sigma is 35.6, and a normal distribution model is established. As shown in fig. 5.
Step S5, setting confidence level value and calculating confidence interval lower limit IminU-2 σ and confidence interval upper bound ImaxU +2 σ; confidence level value pcThe setting was 95%.
Step S6, the current and data I collected and calculated on the same day and the confidence interval lower limit I calculated in the step S5min344.8 and an upper confidence interval limit ImaxComparative analysis was carried out at u +2 σ 487.2, when I < IminWhen I is larger than I, the day is judged to be in a low-capacity statemaxWhen the current day is high-yield, when Imin≤I≤ImaxThe current day is judged to be in a normal capacity state.
The specific steps for judging the current and the value I on the same day are as follows:
judging the production of the factory according to the confidence interval of the obtained normal distribution model, wherein the sum of the currents is less than IminU-2 σ 344.8 indicates that the plant is in a low capacity state, greater than ImaxThe interval u +2 σ 487.2 indicates that the plant is in a high-capacity state, and the interval between the two indicates that the plant is in a normal-capacity state; as shown in fig. 6.
Current and data at day 155 were 296.2, less than Imin344, and thus belongs to a low capacity state;
the current and data of days 1 to 154 are taken as historical samples for establishing an evaluation model to evaluate the capacity condition of day 155. And then taking the current and the data of 2-155 days as historical samples for establishing an evaluation model to evaluate the capacity condition of 156 days, and the like. The updating condition of the evaluation model is shown in fig. 7, wherein r1 and r7 are the upper and lower limits of the original capacity evaluation model; r3, r9 are the upper and lower limits of the performance assessment model over time with old samples removed and new samples added. The shaded part is an area with different model identification results before and after updating.
And then, evaluating the capacity conditions of 156 th and 204 th days by the same steps to test the effectiveness of the method, wherein the current and the data of 2-155 days are used as historical samples for establishing an evaluation model to evaluate the capacity conditions of 156 th day, and the like until the evaluation of the samples of 204 th day is finished. The evaluation result is shown in fig. 8, and the evaluation result of the invention has a good effect on the productivity from the viewpoint of the intuitive judgment of the evaluation result and the actual production state known by the communication with the manufacturer.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (7)
1. A factory capacity evaluation method based on bus current data is characterized by comprising the following steps:
step S1, collecting instantaneous current data of the factory power supply bus at regular time, calculating an average value i (k) of the instantaneous current data meeting preset conditions, where k is 1, 2, …, m, and using the average value i (k) as current value data collected this time;
step S2, selecting all current value data collected in N days before the current day as samples, and calculating current and data i (h) ═ Σ i (k) of each day in the previous N days, h ═ 1, 2, …, N;
step S3, selecting the maximum value Imax (h) and the minimum value Imin (h) from all the current and data I (h) in step S2, dividing K intervals between the maximum value Imax (h) and the minimum value Imin (h), and calculating the current of each day in the previous N days and the probability value p of each intervaliN (j)/N, (i ═ 1, 2, …, K), N (j), j ═ 1, 2, …, K; a total of K intervals, n (j), j being 1, 2, …, K being the current and the number of i (h) falling within each interval;
step S4, based on the probability value p obtained in step S3iConstruction of Normal distribution model M (mu, sigma)2)；
Step S5, setting confidence level value and calculating confidence interval lower limit IminU-2 σ and confidence interval upper bound Imax＝u+2σ；
Step S6, the current and data I collected and calculated on the same day and the confidence interval lower limit I calculated in the step S5minU-2 σ and confidence interval upper bound ImaxCarrying out comparison analysis when the I is less than IminJudging that the current day is in a low-capacity state, and judging that the current day is in a low-capacity state when I is larger than ImaxWhen the current day is high-yield, when Imin≤I≤ImaxThe current day is judged to be in a normal capacity state.
2. The method of claim 1, wherein the method comprises: the preset condition in step S1 is that the sampling rate of the instantaneous current data is fsAnd the acquisition time is T.
3. The method of claim 1, wherein the method comprises: in step S4, the probability value p is usediConstruction of Normal distribution model M (mu, sigma)2) Then, the probability value p is fitted by the least square methodiTo obtain a normal distribution model M (mu, sigma)2)。
4. The method of claim 3, wherein the normal distribution model M (μ, σ) is constructed in the step S42) The method comprises the following specific steps:
step S4.1, new abscissa x ═ I with the midpoint of K intervals in step S3min(h)+(Imax(h)-Imin(h))/(2*K),Imin(h)+3*(Imax(h)-Imin(h))/(2*K),…,Imin(h)+(2*K-1)(Imax(h)-Imin(h) /(2 x K), and comparing the probability value p obtained in step S3iIs divided by (I)max(h)-Imin(h) K) as new ordinate y;
and S4.2, establishing objective function solving parameters and fitting to obtain a normal distribution model with the mean value u and the variance sigma by taking the new coordinates (x, y) as observed quantity data.
5. The method of claim 4, wherein the objective function is formulated as follows:
y＝f(x,w)
wherein x is [ x ]1，x2，…，xn]T∈Rn，y＝R，w＝[w1，w2，…wm]TIs a matrix of parameters to be determined, and n>m, when the objective function takes the minimum value, corresponding to w ═ w1，w2，…wm]TFitting to obtain a normal distribution model with the mean value u and the variance sigma for the parameters; n denotes the number of observation samples, m denotes the number of parameters in the fitting function, R denotes a one-dimensional real number, RnAnd (3) representing n-dimensional real numbers, y is an observed value, and f (x, w) is a fitting function value corresponding to the fitting function relation.
6. The method of claim 1, wherein the method comprises: the confidence level value pcThe setting was 95%.
7. The method of claim 1, wherein the method comprises: in the step S1, instantaneous current data of the plant power supply bus is collected at regular time, specifically, data collection is performed by hanging a current caliper sensor on the plant power supply bus.
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