Processor for process control

Abstract: A processor for measured process information comprises a fea­ ture extracting section (4) and an abstract expression section (5). In order to easily identify a pattern of time-series state changes based on measured information so as to facili­ tate representation of edge portions of an image, features ex­ tracted in form of expansion coefficients of polynomials from the measured information are translated into abstract expres­ sions so as to derive from the measured information signi­ ficant control information. Features extracted in form of ex­ pansion coefficients from brightness changes in the vicinity of each pixel constituting image data are also translated into abstract expressions.

Description: BACKGROUND OF THE INVENTION The present invention relates to a processor for processing measured process information suitable for achieving a computer management or control on complicated processes such as a thermal process in an iron manufacturing furnace and a chemical reaction process in a chemical plant. In many industrial manufacturing processes, a system measuring and controlling states of the system have been broadly adopted. In a typical system, for example, the PID control is achieved by use of a deviation between a state of a process and a target value thereof or an application method of an artificial or man-created intelligence in which rules of "if ... then ..." are applied to a quantity of a state of a process so as to attain a control quantity through an inference. In these control apparatus, the signal processing of measured process information has been 5 limited to an arithmetic processing of instantaneous items of measured data such as a removal of a noise from measured data and a conversion of the measured data into physically significant numeric value. Furthermore, a monitor device for processing an image attained from an image input unit such as an ITV so as to decide the presence or absence of an invading object, a system for extracting, from an image, information related to a distance and an image of a moving object, and an equipment for recognizing characters and graphics have been put to practices. The typical processing in an image processing apparatus employed in such a device is limited to the arithmetic processing to extract an edge, which will be described later, and to effect arithmetic processing by use of the edge. Namely, in the processing, for example, a portion or an edge where a brightness abruptly varies is extracted from the image to determine the presence or absence of an object based on a position of the edge and to attain a speed of a moving object from a change of the edge. In consequence, in order to recognize an image, a complicated processing has been necessary in which a histogram indicating a distribution of edges is generated to achieve a statistic processing or in which connective relationships between certain pixels associated with edges are sequentially checked to extract line segments so as to determine a direction of the line segments. In the signal processing apparatus of the prior art technology, there has been a problem that although instantaneous state of measured data can be attained, it is difficult to represent a time-series change pattern thereof. In consequence, in the conventional technology, a stabilizing control can be achieved to overcome various disturbances in stationary process states occupying most part of the operation period; however, a nonstationary state or a sudden abnormality cannot be easily coped with and a control operation for the restoration is attended with difficulties. The operation of this kind has been manually conducted by the human operator. From Patents Abstract of Japan vol. 6, No. 161 (M-151) 24 August 1982 & JP-A-57 075 214 (Mitsubishi Electric Corporation), 11 May 1982, a processor for controlling a process on the basis of process data imformation including the features of the first part of claim 1 is known. Automatisierungstechnik - AT, vol. 33, No. 2, February 1985, München DE, pages 45-52, XP290267, B.Bieker et al. "Fuzzy Regelungen und linguistische Regelalgorithmen - eine kritische Bestandsaufnahme", and Information Sciences 36, 59-83 (1985), pages 59 - 83; Michio Sugeno "An Introductory Survey of Fuzzy control", describe fuzzy control methods. SUMMARY OF THE INVENTION It is an object of the invention to provide a processor for a fast and reliable control of a process on the basis of process data information, in particular during changes of state. This object is met by a processor according to claim 1. Preferred embodiments are disclosed in the depending claims. A measured process information processor according to the invention includes a feature extracting section for achieving a signal processing to extract a feature from measured information and an abstract expression section for converting or translating the feature into an abstract expression, thereby detecting significant information from measured values conventionally not utilized so as to employ the information for the control. In addition, the processor comprises a symbolizing section for extracting characteristic points from the measured information so as to subdivide the measured information into partitions at the characteristic points, to obtain an abstract representation of each partition, and to convert the abstract representation into symbols, and a causality inference section for inferring the causality of the measured information thus symbolized, thereby determining a control operation. As a result, significant information which has not been used in the measured information in the conventional technology is detected to be utilized as information for the control. By providing the processor with a data storage section for accumulating therein the result obtained by symbolizing the measured information in the symbolize section, the measured information can be efficiently stored therein. Moreover, when the processor is provided with a pattern matching section for achieving a symbolic matching on the measured information accumulated in the data storage section and a classifying/retrieving section for conducting classification and retrieval of the measured information based on a result of the matching, empirical knowledge necessary for the control can be easily extracted from the measured information of the process. BRIEF DESCRIPTION OF THE DRAWINGS The advantages of the present invention will become apparent by reference to the following description and accompanying drawings wherein: FIG. 1 is a schematic block diagram showing a first embodiment of a process control system according to the present invention; FIG. 2 is a block diagram showing a flow of processing of a signal processor according to the present invention; FIG. 3 is a graph showing an example of a filter kernel; FIGS. 4A and 4B are a schematic diagram showing an example of conversion or translation rules; FIG. 5 is a graph showing another example of a filter kernel; FIGS. 6A and 6B are a diagram showing an example of a weight function to determine a filter kernel; FIG. 7 is a schematic block diagram showing a second embodiment of a process control system according to the present invention; FIG. 8 is a block diagram showing a flow of processing of a symbolizing section 74; FIG. 9 is a flowchart showing a processing procedure of the symbolizing section 74; FIG. 10 is a block diagram showing a causality inference section 76 and a causality storage section 77; FIG. 11 is a block diagram showing the constitution of an alternative embodiment according to the present invention; DESCRIPTION OF THE PREFERRED EMBODIMENTS Referring now to the drawings, description will be given of the various sections such as the feature extracting section of an embodiment of a process control system according to the present invention. In the feature extracting section, for data of measured process information measured at respective points of time, a state change pattern in the neighborhood of a point including the data is resolved into a predetermined combination of appropriate polynomials. As a result, a noise of a measured value unexpressive with a polynomial can be removed and features of the data can be expressed with coefficients combining polynomials with each other. By sequentially processing data, which changes in a time-series fashion, at the respective points of time, a change pattern in which features of the time-series data are expressed is attained by use of the feature extracting section. In the abstract expression section, based on the change pattern of the features of the time-series data produced from the feature extracting section, a predetermined combination of appropriate feature changes is translated or converted into an abstract expression. As described above, the signal processor according to the present invention has a remarkable characteristic that the noise included in the measured value due to the nature of the process can be efficiently removed and an arbitrary abstract expression can be created for each measured data. In addition, in the symbolizing section, characteristic points are extracted from the changes developed in the measured process information with respect to time so as to splitting the measured process information at the characteristic points, thereby achieving the symbolizing operation. Through the processing above, a noise contained in the measured information is removed so as to attain a symbolic expression. Furthermore, the symbolic expression of the measured information facilitates estimation of causality and the like included in the measured information. This enables the control to be achieved by use of significant information contained in the measured information which has not been utilized in the conventional system. With the provision of the data storage section for accumulating therein the measured information thus symbolized, the data to be accumulated is symbolically compressed; in consequence, the efficiency of the data accumulation is abruptly increased. Moreover, since the accumulated data is beforehand translated into symbols, judgement of analogy to be conducted by use of a symbolic pattern matching is facilitated. As a result, the classification and retrieval of the information measured in the past operations become to be possible, and hence by extracting empirical knowledge necessary for the control, the control performance can be considerably improved. Referring next to FIGS. 1 to 6B, a description will be given of the first embodiment of a signal processor for processing measured process information according to the present invention. FIG. 1 is a block diagram showing an embodiment of a process control system employing a signal processor according to the present invention. In the configuration of FIG. 1, process data including measured information related to a process 10 is collected by a process data section 1 having an input/output system so as to store the process data in a data accumulator section 2. The measured information is then processed by a signal processor 3, which comprises a feature extracting section 4 for extracting features from the measured information, an abstract expression section 5 for translating the features into an abstract expression, a parameter storage section 6 for storing therein a filter kernel to be used in the feature extracting section 4, and an abstract information storage section 7 for storing therein translation rules linking the abstract expression to the features to be utilized in the abstract expression section 5. Furthermore, the information produced from the abstract expression section 5 is processed by a dedicated controller 8 to control the process 10. The information from the abstract expression section 5 is fed to a man-machine interface 9, which determines the storage contents of the parameter storage section 6 and the abstract information storage section 7 and a control content of the dedicated controller 8 by use of the information. The signal processor 3 includes components such as a microcomputer circuit and a software system materialized in a read-only memory (ROM). The operation of the process control system of FIG. 1 associated with the configuration above will be described in conjunction with cases where the control is achieved and where the control apparatus is designed. In the control operation, the signal processor 3 conducts the following operations (a) and (b). (a) The feature extracting section 4 filters the measured process information collected by the process data section 1 by use of the filter kernel beforehand stored in the parameter storage section 6, thereby expanding the measured information into a polynomial. (b) The abstract expression section 5 converts the measured information into an abstract expression by use of expansion coefficients for the polynomial obtained from the feature extracting section 4 and the translation rules stored in the abstract information storage section 7, thereby transferring the abstract expression to the dedicated controller 8. The operations (a) and (b) will be next further described in detail. First, a description will be given in detail of the operation (a) with reference to FIG. 2. Between the measured process information 11 obtained from the process data section 1 and a filter kernel 12 stored in the parameter storage section 6, a product sum operation 13 is conducted to expand the information into a polynomial. The filter kernel 12 is determined in the following method. Assume that the change with respect to time of the measured process information to be expanded is expressed as f(t) and that a polynomial of degree n is represented as H n (x) (n = 0, 1, 2, ...). For simplicity of explanation, the present time t is set to 0 and description will be given of a case where f(t) is expanded up to the point t = 0. Moreover, a weight function to obtain a smaller value for a larger distance from x = 0 is to be expresed as E(x). The polynomial H n (x) of degree n employed for the expansion of the measured process information f(t) is determined to satisfy the following expressions (1) and (2). The polynomial H n (x) is uniquely determined as follows when E(x) is given. Where, a is a constant determining a range in which the measured process information f(t) is expanded. In this situation, let us obtain a filter kernel W n (x) (n = 0, 1, 2, ...) as determined by the following expression (3). W n (x) = H n (x) E(x) (x = a, a + 1, ..., 0) Assume here that the product sum between the measured process information f(t) up to t = 0 and the kernel W n (x) is S n . n = 0, 1, 2, ... Under this condition, the measured process information f(t) is expanded into a polynomial as follows. f(t) = S 0 H 0 (t) + S 1 H 1 (t) + S 2 H 2 (t) ... A description will be given in detail of the expression (5) which holds in this case. Assume that a system of orthonormal functions in a situation where the expressions (1) and (2) are satisfied is Ψ n (x) (n=0, 1, 2, ...), then Ψ n (x) is expressed as follows. Ψ n (x) = Hn(x) {E(x)} 1/2 By use of the system orthonormal functions, f(t){E(t)} 1/2 is expanded into the following expression (7). f(t) . {E(t)} 1/2 = A 0 Ψ 0 (t) + A 1 Ψ 1 (t) + A 2 Ψ 2 (t) ... Where, the expansion coefficient A n (n = 0, 1, 2, ...) is expressed as follows. Let us transform the expansion coefficient A n . Namely, the result of the product sum operation S n becomes identical to the expansion coefficient A n . In consequence, applying the expressions (6) and (9) to the expression (7), the following expression (10) results. f(t){E(t)} 1/2 = [S 0 H 0 (t) + S 1 H 1 (t) + S 2 H 2 (t) + ...] {E(t)} 1/2 This is reduced as follows, and hence the expression (5) holds. f(t) = S 0 H 0 (t) + S 1 H 1 (t) + S 2 H 2 (t) + ... FIG. 3 shows a filter W n (x) (n = 0, 1, 2) in a case where the following exponential function is used an example of the weight function, σ = 2.0, and the constant a to determine the expansion range is set to -10. E(x) = exp ( x σ ) As can be seen from the form of the filter kernel W n (x) of FIG. 3; W 0 (x), W 1 (x), and W 2 (x) are considered to supply an average value, an average gradient, and an average horizontal wavy change in the neighborhood of x = 0. Referring next to FIG. 2, a description will be given in detail of the operation (b) above. The abstract expression section 5 collates polynomial expansion coefficients 14 sent from the feature extracting section 4 with translation rules 15 stored in the abstract information storage section 7 so as to achieve a data translation 16 to obtain an abstract expression 17. FIG. 4A and 4B show details of the translation rules 15. In the expansion coefficients 14, S 0 indicates a level of a process state. Depending on the value of S 0 as shown in FIG. 4A, an average level of the process state can be translated into an abstract expression. The abstract expressions of FIGS. 4A and 4B respectively are associated with the following abbreviations. PB: Positive big PM: Positive medium PS: Positive small ZR: Zero (small value) NS: Negative small NM: Negative medium NB: Negative big Furthermore, in the expansion coefficients 14, S 1 and S 2 respectively denote an average gradient and an average degree of horizontal wavy change respectively of the process state. In consequence, as shown in FIG. 4B, based on a combination of the expansion coefficients S 1 and S 2 , an abstract expression of a change in the process state can be obtained. Let us assume that a change of the process state from α to β is represented by (α, β), where the combination " α, β" stands for one of the following abstract expressions. ++: Abrupt increase +: Increase 0: Equilibrium -: Decrease --: Abrupt decrease The abstract expression 17 naturally includes, in addition to the qualititative expression method as described above, a fuzzy expression conforming to a standard such as a membership function of the fuzzy logic or the like beforehand supplied to the system. The abstract expression 17 created through the operations in FIGS. 4A and 4B is fed to the dedicated controller 8 so as to determine the control quantity on the process 10. The dedicated controller 8 executes processing as follows. (i) Identification of state Based on the abstract expression 17, the controller 8 determines whether or not the process is in a stationary state. (ii) Stabilizing control When the process is judged to be in the stationary state, the controller 8 achieves a stabilizing control in conformity with "a qualitative causality" registered to the controller 8. (iii) Abnormality restoring control When the process is judged to be in other than the stationary state or in an abnormal state, the controller conducts an inference based on a restore processing rule registered to the controller 8 so as to determine a restoring control operation. In order to enhance the function of the dedicated controller 8, a one-step forecast value of the process state may be included in the outputs from the signal processor 3. The one-step forecast value of the process state can be easily attained by assigning t + 1 for t in the expression (11). As a result, the processing steps (i) to (iii) can be achieved depending on the forecast of the process state. In the control operation, the man-machine interface 9 edits, in response to an information acquisition request issued from an operator, a process engineer, a process manager, or the like, the abstract expression received from the abstract expression section 5 so as to supply the person requesting the information with a result of the edit operation by use of report means such as a cathod-ray tube (CRT). As a result, the person who has issued the request can obtain the process state in an abstract expression (in a language, symbols, etc.) to be easily understood by a human, the process states including the abnormal state can be appropriately identified at a high speed. The operation of the control system of FIG. 1 in the control operation has been described. Next, a description will be given of operations of the respective components of FIG. 1 in the control system design. When designing the control system, the signal processor 3 conducts the following operations (c) and (d). (c) The feature extracting section 4 filters signals of the measured process information 11 stored in the data accumulator section 2 by use of the filter kernel 12 beforehand stored in the parameter storage section 6, thereby developing the measured process information 11 into a polynomial. (d) The abstract expression section 5 processes the measured information 11 by use of the polynomial expansion coefficients attained from the feature extracting section 4 and the translation rules 15 stored in the abstract information storage section 7 so as to obtain an abstract expression, which is then sent to the man-machine interface. Next, a description will be given in detail of the operation of the man-machine interface 9. The man-machine interface 9 conducts the following operation steps. (iv) In order to attain an abstract expression depending on a special state of the process, the interface 9 updates the filter kernel 12 stored in the parameter storage section 6 and the translation rules 15 kept in the abstract information storage section 7. (v) By use of the abstract expression of multiple variables representing a process state, the interface 9 obtains "a qualitative causality" between the variables based on the correspondences between the variables including a time delay so as to register the causality to the dedicated controller 8. (vi) Knowledge related to a control of an objective process possessed by experts is registered as rules in a form of "if ... then ..." to the dedicated controller 8. the operation steps (iv) and (v) may be interactively achieved in a trail-and-error fashion or may be automatically conducted by use of an expert system related to a processing procedure. The operation step (vi) is achieved in an interactive manner. The operations of the constituent sections of the signal processor in the control apparatus design have been described. When designing a control apparatus, the measured process information is beforehand stored in the data accumulator section 2; consequently, the filter kernel 12 of the parameter storage section 6 may have a center on x = 0 so as to expand toward both directions therefrom. FIG. 5 shows a case where the zone of x for the total operation Σ is conducted in the expressions (1), (2), (4), (8), and (9) ranges from -10 to 10, E(x) of the expression (12) is as follows, and σ is set to 2. E(x) = exp - ( x 2 2σ 2 ) As the weight function E(x), in addition to the exponential functions contained in the expression (12) and (13), an arbitrary function may be utilized. For example, the following lines E(x) as shown in FIG. 6A may be employed. E(x) = 1 ± x / σ Moreover, the following parabola E(x) as shown in FIG. 6B may be used. E(x) = σ 2 - X 2 The value of the parameter σ in each of these functions is required to be determined depending on the magnitude of the noise contained in the measured data and the sampling period thereof. The maximum value of the degree n of the polynomial employed to the features need only range from two to four. In the example the embodiment above, each point of time associated with the measured data is utilized as the standard or reference to expand the information into a polynomial. As an alternative embodiment, it is also naturally possible to split the data into partitions having an arbitrary interval length such that the measured data of each interval is developed into a polynomial so as to produce an abstract expression for each interval, thereby attaining the similar function. Referring next to FIGS. 7 to 11, a description will be given of the second embodiment of a signal processor for processing measured process information according to the present invention. FIG. 7 is a block diagram showing an embodiment of a control apparatus employing the signal processor according to the present invention. In this system, data from a process data section 1 having an input/output system to collect measured information is stored in a data accumulator section 2. The configuration includes a signal processor 3, which comprises a symbolizing section 74 for extracting features from the measured information so as to convert or to translate the information into symbols, a symbolization dictionary 75 for storing therein translation rules linking a filter kernel, features, and symbols to be used in the symbolizing section 74, a causality inference section 76 for conducting an inference to attain a causality between process data items, and a causality storage section 77 for storing therein a causality between the process data items. Moreover, this system includes a dedicated controller section 8 including integrated microcomputer circuit and the like, a CRT for displaying thereon a control output from the dedicated controller 8, and a man-machine interface section 9 having a printer or the like. The signal processor 3 comprises electric circuits including a microcomputer circuits and a software system materialized in an ROM. Next, a description will be given of the operation of the process control apparatus having the constitution above. The symbolizing section 74 achieves the processing (a) to (c) as follows. (a) By use of the filter kernel beforehand stored in the symbolizing dictionary 75, a signal filtering operation is conducted on the measured process information collected by the process data section 1 so as to extract characteristic points from the measured process data. (b) The measured process information is split into partitions or intervals at the characteristic points determined in the processing (a) so as to translate each partition into an abstract expression. (c) A sequence of abstract expressions obtained through the processing (b) in a time series fashion is further converted into symbolic expressions such as abstract step changes, impulse changes, and oscillating or vibrating states. Referring next to FIG. 8, a description will be given in detail of the processing (a) to (c) of the symbolizing section 74. First, a product sum is computed between the measured process data 21 attained from the process data section 1 or stored in the data accumulator section 2 and a filter kernel 22 kept in the symbolizing dictionary 75, thereby extracting characteristic points from the measured process data 21. The filter kernel 22 is as follows, for example. W 2 (i) = 1 4 2Π σ 3 i 2 - σ 2 . exp (- i 2 2σ 2 ) (i = -a, -a + 1, ..., a) Where, σ is an appropriate constant and a is a constant indicating a region or a width of the filter. The product sum between the measured process data 21 and the filter kernel 22 is conducted through an expression (17). Based on a result 23 of the product sum operation, the system extracts characteristic points satisfying an expression (18) in the following cases. In the case of the embodiment of FIG. 8, by use of the product sum result 23, points of time t 1 to t 5 can be extracted as characteristic points. The processing (a) of the symbolizing section 74 has been described in detail. Next, a description will be given in detail of the processing (b) of the symbolizing section 74. First, an average value of the measured process data in the vicinity of each of the characteristic points is computed by use of an expression (20). Where, S 0 (t j ) is a mean value or an average value of the measured process data in the neighborhood of a characteristic point t j and W 0 (i) denotes a filter kernel stored in the symbolizing dictionary 75. Through an execution of the operation represented by an expression (21) at each characteristic point, a measured value E(t j ) from which the noise contained in the measured process data has been removed can be attained at the characteristic point. E t j = 1 2Π σ ·S 0 t j - 1 4 2Π σ . S2 t j The expression (21) means that by use of a mean value S 0 (t j ) of the measured process data f(t) in the neighborhood of the characteristic point t j and an average horizontal wavy change value S 2 (t j ) thereof, a state in the vicinity of the characteristic point t j is restored such that assuming i to be 0, the value is obtained at the characteristic point t j . Next, by use of the measured value E(t j ) at the characteristic point, the system obtains a gradient of the measured process data in an interval between characteristic points based on an expression (22). G(t j , t j+1 ) = E t j+1 - E t j t j+1 - t j Where, G(t j , t j+1 ) stands for a gradient in an interval between characteristic points t j and t j+1 . With the gradient G(t j , t j+1 ), each interval between the characteristic points can be translated into an abstract expression. For example, assuming that the following translation rules are stored in the symbolizing dictionary 75. (i) G(t j , t j+1 ) ≥ℓ 2 ... Abrupt increase (ii) ℓ 1 ≤G(t j , t j+1 ) ℓ 2 ... Increase (iii) -ℓ 1 <G(t j , t j+1 ) <ℓ 1 ... Equilibrium (iv) -ℓ 2 <G(t j , t j+1 ) ≤-ℓ 1 ... Decrease (v) G(t j , t j+1 ) ≤-ℓ 1 ... Abrupt decrease The rules are applied to the result of the operation of the expression (22) so as to attain an abstract expression 24 for each interval. The processing (b) of the symbolizing section 74 has been described in detail. Next, the processing (c) of the symbolizing section 74 will be described in detail. The abstract expression for each interval between the characteristic points is processed by use of translation rules stored in the symbolizing dictionary 75 to produce symbolic expressions. As the translation rules, for example, rules in the form of "if ... then ..." are stored in association with symbols to be represented. (i) if (Equilibrium, (Abrupt increase, Abrupt decrease), Equilibrium) then (Equilibrium, Impulse change, Equilibrium) (ii) if (Equilibrium, Abrupt increase, Equilibrium) then (Equilibrium, Step change, Equilibrium) (iii) if (#, (Abrupt increase, Abrupt decrease))then (Oscillation) In the parentheses of the if part of this format, abstract expressions are arranged in a time-series fashion. This representation indicates that when a series of abstract expressions in the if part exist in the abstract expression 24, the expression in the parentheses are translated into a sequence of symbols in the parentheses of the then part. The number mark # used in the if part of the translation rule (iii) means that the abstract expression in the parentheses following the mark may reapeatedly appear as many times necessary. The processing (c) of the symbolizing section 74 has been described in detail. FIG. 9 shows a flowchart of the processing (a) to (c) of the symbolizing section 74. In the description of this embodiment, σ of the filter kernels W 2 (i) and W 0 (i) respectively of the expressions (16) and (20) has been assumed to be a constant. In a case where the measured process data is associated with high-speed and low-speed changes to develop complicated variations, the processing (a) and (b) may be executed with several different kinds of σ so as to obtain an integrated data of the resepctive results. Furthermore, the abstract and symbolic expressions in the symbolizing section 74 may naturally include, in addition to the qualitative expression method described above, a fuzzy representation based on the standard such as a membership of a fuzzy logic beforehand supplied to the system. A description will be given here in detail of the operation of the causality inference section 76 and the causality storage section 77. As shown in FIG. 10, the causality inference section 76 includes a symbolized data storage section 41, an inference engine 42, and a job storage section 43. The causality storage section 77 comprises a state indentification rule storage section 44, a causality rule storage section 45, and an abnormal event judge rule storage section 46. Various process data symbolized in the symbolizing section 74 are stored in the symbolized data storage section 41. The inference engine 42 executes the following processing. (i) Identification of state Based on the contents respectively of the symbolized data storage section 41 and the state identification rule storage section 44, the inference engine 42 judges to determine whether or not the process is in the stationary state. (ii) Stabilizing control When the process is determined to be in the stationary state as a result of the judgement, by use of the content of the causality rule storage section 45, the inference engine 42 determines a control parameter for a stabilizing control according to the causality. (iii) Abnormality restoring control When the process is determined to be in other than the stationary state or in the abnormal state as a result of the judgement, by use of the content of the abnormal event judge rule storage section 46, the inference engine 42 determines the abnormal event so as to decide a restoring control associated with the abnormal event. The job storage section is loaded with a temporary hypothesis obtained in the inference engine 42. The hypothesis is updated through an inference procedure. The dedicated controller 8 generates various control instructions for a process control based on the results of processing achieved by the causality inference section 76, thereby executing the process control. The man-machine interface 9 notifies, in response to an information acquisition request from an operator, a process engineer, a process manager, or the like, such information items as a result obtained by editing the symbolic expression from the symbolizing section 74 and a process state attained from the causality inference section 74 by use of a CRT or the like to the person who has issued the request. As a result, the pertinent person can attain the process state in an abstract expression (a language, symbols, and the like) to be easily understood by a human, which enables the abnormal state and the like to be appropriately identified at a high speed. FIG. 11 is a block diagram showing an alternative embodiment of the signal processor in accordance with the present invention. The system includes a process data section 51 having an input/output system to collect measured information, a symbolizing section 52 for extracting features from the measured information to translate the information into symbols, and a symbolizing dictionary 53 for storing therein translation rules to be used in the symbolizing section 52 for the translation associated with a filter kernel and the symbols. In the configuration, the process data section 51, the symbolizing section 52, and the symbolizing dictionary 53 respectively have the same functions as the corresponding components of FIG. 7. Various measured data symbolized in the symbolzing section 52 are stored in the data storage section 54. In order to symbolically store the measured data in a compressed state, it is possible to efficiently use a storage device of a data accumulator section 54. The system further includes a pattern matching section 55, which compares a standard pattern stored in a classification/retrieval section 56 via a man-machine interface 57 with measured data accumulated in the data accumulator section 54 so as to compute a symbolic similarity therebetween. As a result, a great amount of measured data accumulated in the data accumulator section 54 can be effectively classified and hence can be retrieved, which enables empirical knowledge necessary for the control to be extracted from the measured information accumulated through the operations conducted in the past. By use of the apparatus in accordance with the present invention, a complicated process control can be remarkably improved as follows. (1) A pattern of time-series state change in the measured process information which cannot be directly measured in the prior art technology can be employed as significant information to judge the process state change. (2) A problem associated with the processing of interference between variables which has been a hindrancewith respect to information when configuring a process model and a problem related to the limitation of numeric computations in a multi-variable model can be solved by translating states of variables into abstract expressions such as symbols so as to use the states in the control operation. (3) By translating the process data into abstract expressions to be used in the control operation, the accumulated process data can be easily classified and retrieved; furthermore, control rules necessary for the control of an objective process can be registered and used in a form which retains integrity thereof. (4) Since the process state is approximated by use of a polynomial or is translated into symbols for the control operation, a high-frequency noise included in the measured value can be removed and hence the process state is correctly determined at a high speed. (5) Since information related to the process can be presented in an abstract expression (a language, symbols, etc.) to be easily understood by a human, it is possible to construct a man-machine interface having a high visibility to recognize the process state. (6) An effective analysis can be achieved at a high speed also in systems, for example, in a system to be employed in a department store or a shop for a control of articles or in a security facility to handle several thousand kinds of stock prices, namely, in a system in which more than 10 4 time-series variables are monitored to decide a management plan or a sales sterategy.

Claims: A processor for controlling a process on the basis of process data information obtained from said process, said processor comprising process data collecting means (1, 51) for collecting process data information, data accumulating means (2, 54) connected to said process data collecting means for storing said process data information, processing means (3) connected to said process data collecting means and said data accumulating means for processing said data information to develop said process data information to extract features therefrom, and controlling means (8) connected to said processing means (3) for providing process control signals to said process based on outputs of said processing means, characterised in that said processing means (3) includes parameter storage means (6, 75) for storing a series of values in a form of a predetermined filter kernel, feature extracting means (4, 74) for providing by operation between said series of values of said predetermined filter kernel and said process data information a plurality of product sum data in a sequence, said product sum data constituting polynomials with a respective coefficient which is extracted, abstract information storage means (7, 77) for storing combinations of a data value and an abstract expression and abstract expression means (5, 76) for translating said extracted coefficients into a sequence of abstract expressions by referring to said abstract information storage means. The processor according to claim 1, wherein said feature extracting means (4, 74) includes forecasting means for forecasting a change in the state of said process based on an increase of the variable of the polynomials in said plurality of product sum data. The processor according to claim 2, wherein first three polynomials of said plurality of product sum data are used in said abstract expression means (5, 76). The processor according to any of claims 1 to 3, wherein said feature extracting means (74) further comprises splitting means for splitting said process data information into partitions each relating to a time interval, said abstract information storage means (77) further comprises causality storage means (45) for holding a plurality of knowledge rules indicating causalities of changes in said process, and said abstract expression means (76) further comprises causality inference means (42) for inferring causalities by use of said knowledge rules. The processor according to claim 4, wherein said causality inference means (42) includes pattern matching means (55) for obtaining a correspondence relationship between said abstract expressions and classification and retrieval means (56) for classifying and retrieving information based on the correspondence relationship. The processor according to claim 4 or 5, wherein said abstract expression means (5, 76) further comprises editing means for editing a translated sequence of abstract expressions in response to a request of a man/machine interface (57) and report means for reporting the output of the editing means via said man/machine interface. The processor according to any of claims 4 to 6, wherein said causality inference means (42) extracts knowledge rules for control of said process by using said abstract expressions. The processor according to any of claims 4 to 7, wherein said abstract information storage means (76) further comprises rule storage means (44) for holding rules each including a further abstract expression regarding stationary and nonstationary states of said process and wherein said causality inference means (42) judges a state of said process by using said further abstract expression from said rule storage means.

Inventors: FUNABASHI MOTOHISA, NISHIYA TAKUSHI, FUNABASHI, MOTOHISA, NISHIYA, TAKUSHI
Applicants: HITACHI LTD, HITACHI, LTD.
Application year: 1989
Main classification: G05B13
IPC codes: G05B13, G06T9, G06T7, G06K9